Automated and reliable determination of a forward value associated with a future time period based on objectively determined expectations related thereto

ABSTRACT

The disclosed embodiments relate to computing a forward interest rate for a select future time period subsequent to a current date, such as 1 month, 3 month, 6 month or 12 month term, utilizing data observed or otherwise derived from the trading of futures contracts having short term interest rate based underliers, e.g. based on overnight interest rates, and, in one embodiment, are integrated with an electronic transaction processing system, e.g. an electronic trading system, to access data indicative of the trading thereof, and therefore avoid reliance upon subjective/opinion inputs. Generally, the disclosed embodiments generate a model of expected interest rates for every day of the time period for which a forward interest rate is desired based on a set of interest rate futures contract whose expiration periods cover the period. The disclosed embodiments enable automated determination of a stable, replicatable and risk-free short term forward reference rate which further eliminates the inherent issues with LIBOR discussed above.

BACKGROUND

An interest rate is the price of money. A backward interest rate isprice of money for time period prior to the time that the rate is set,e.g. for the prior day also referred to as an overnight rate. A forwardinterest rate is the price, determined at the time the rate is set, ofmoney for a future time period, e.g. the rate represents today's cost offuture money. Short term interest rates are interest rates typicallyused for debt with future maturities less than one year from the currentdate and are typically administered by the central banks of nations,where, as will be explained, the different rates set by differentnational institutions may be used for different purposes. As opposed toshort term interest rates, long term interest rates, for use with debthaving maturities greater than 1 year, are typically set by marketforces, i.e. through the bilateral negotiations of self-interestedparties looking to transact based on the interest rate. Two examples ofnational institutionally set short-term interest rates are the FederalFunds (“Fed Funds”) rate and the London Interbank Offered (“LIBOR”)rate.

The Federal funds rate is the rate at which U.S. banks lend money toeach other overnight. The money in question is the reserves that sit intheir bank accounts in the Federal Reserve system. If Bank A has excessreserves at the end of the day and Bank B has a reserve deficit at theend of the day (reserves are the money they have to keep onhand—electronically, at least—in case people ask for it; reserverequirements are set by the Federal Reserve), Bank A will loan the moneyto Bank B for a period of one day. The rate of interest Bank A willcharge is the Federal funds rate.

The actual federal funds rate is set by the open market as banks loaneach other money from day to day. The federal funds rate “target,” onthe other hand, is set by the Federal Open Market Committee (“FOMC”),which is headed by the Federal Reserve Chairman. Generally, whencommentators refer to the federal funds rate, they're usually referringto the target rate set by the Fed, rather than to the actual federalfunds rate. The FOMC meets every few months and decides whether to raisethe federal funds rate target, lower it or keep it as is.

The target rate is used as a tool to help control the nation's moneysupply and promote employment. For example, if inflation begins to runabove the Federal Reserve's target rate, the Fed may choose to raise thefederal funds rate target. By doing so, the Fed is restricting theamount of money that's available to banks, making it more likely thatthey'll raise interest rates on commercial and consumer loans. That, inturn, should help control the prices of goods and services by dampeningcommercial activity. Meanwhile, if the U.S. economy is sputtering andunemployment is high, the Fed may choose to lower the federal funds ratetarget in order to help bring borrowing costs down and encouragebusinesses and consumers to spend.

The Fed Funds rate is a backwards looking rate in that it is anaggregation of data from the prior day. That is, as opposed to beingpredetermined at the start of a time period, the Fed Funds rate iscomputed based on previously occurring events. Interest rate futurescontracts which are based on the Fed funds rate are typically defined tosettle at the end of a month based on the average effective Fed Fundsrate occurring each day over that month of settlement. Accordingly,while one may not know the settlement price until the settlement date,as the settlement date draws nearer, the settlement price becomes moreand more certain.

The London Interbank Offered Rate (“LIBOR”) is an interest ratebenchmark used as a reference rate for transactions. This reference ratereflects the general cost of large banks' borrowing that is not backedby collateral. U.S. dollar LIBOR plays a central role in the U.S.financial markets and economy. It is used to set interest rates onfinancial products such as mortgages and private student loans. Unlikethe federal funds rate, which only applies to U.S. banks, the LIBOR is aLondon-based international interest rate benchmark used around theglobe.

The difference between LIBOR, formerly known as the London InterbankOffered Rate and now ICE LIBOR (Intercontinental Exchange LIBOR), andbenchmark rates that reflect minimal credit risk, is used as a measureof risk in banks and stress in financial markets.

LIBOR is an average of the estimated interest rate that a high qualitybank in London would be charged to borrow from other leading banks. Inparticular, a sample of multiple, e.g. 16, banks, including Barclays,Chase, Citi, HSBC and Bank of America, report to the British Bankers'Association how much interest they expect to be charged by other banksfor a short-term loan. Banks' interest rate estimates aren't required tobe based on actual transactions. However, the banks are expected to givetheir best guesses.

In essence, LIBOR is a short-term unsecured interest rate chargedbetween banks for wholesale funding. However, LIBOR is also the primarybenchmark for short-term interest rates around the world. LIBOR ratesare calculated for five currencies and seven borrowing periods rangingfrom overnight to one year and are published each business day. DailyLIBOR interest rate fixings have been published since Jan. 1, 1986 andhave since become deeply entrenched into the global financial markets.Many financial institutions, mortgage lenders and credit card agenciesset their own interest rates relative to LIBOR. In fact, over $350trillion dollars' worth of financial derivative contracts, mortgages,bonds and retail and commercial loans have their interest rates tied toLIBOR. Most consumers probably have at least one financial instrument,such as a mortgage, home equity line of credit, or business loan thathas an interest rate tied to LIBOR.

While most small and mid-sized banks borrow federal funds to meet theirreserve requirements—or lend their excess cash—the central bank isn'tthe only place they can go for competitively priced short-term loans.They can also trade Eurodollars, at the LIBOR rate, which areU.S.-dollar denominated deposits at foreign banks. Because of the sizeof their transactions, many larger banks are willing to go overseas ifit means a slightly better rate. For example, the 3 month US Dollar(USD) LIBOR interest rate is the average interest rate at which aselection of banks in London are prepared to lend to one another inAmerican dollars with a maturity of 3 months.

Post financial crisis regulation has significantly reduced bank appetiteto issue commercial paper and wholesale deposits. As such, there is nowa very low volume of transactions for banks to base their LIBORsubmissions and as a result, banks must rely upon their “expertjudgement” translating other interest rates into a LIBOR rate. In fact,submissions based upon “expert judgement” as opposed to realtransactions now make up 70% of the daily three-month LIBOR submissionsaccording to Barclay's Bank. The liability associated with generatingsuch an important and highly utilized interest rate based upon expertjudgement is enormous, especially in the wake of the LIBOR fixingscandals. During this scandal, it was discovered that some banks werefalsely inflating or deflating their rates in order to profit fromtrades or to give the impression that they were more creditworthy thanthey actually were. Accordingly, a replacement for LIBOR is needed.

Removing and replacing LIBOR is an enormously complicated task. Whilethere are trillions of dollars' worth of financial instruments thatreference LIBOR, the largest complication rests is those financialassets and those financial contracts that have a maturity beyond the2021 deadline. As it relates to futures and derivatives contracts, ISDAmaster agreements between counterparties will have to be amended orreplaced. Retail mortgages, home equity lines of credit, and any otherconsumer or business debt tied to LIBOR will have to be amended unless aback-up interest rate index is referenced in the original documentation.Mortgage backed securities, loans and floating rate bonds all tied toLIBOR will have to be addressed contractually, and with regard to dealspecific covenants, may require consents from the owners of thesesecurities. In addition, as previously discussed, all of the partiesinvolved will need to come to some consensus that the compensatingspread, i.e. between LIBOR and the rate/system which replaces it, isfair and reflective of the original interest rate and credit riskimbedded within LIBOR.

The Federal Reserve Board and the Federal Reserve Bank of New Yorkconvened the Alternative Reference Rates Committee (ARRC) to identify analternative to LIBOR. The OFR is a member of ARRC and has collaboratedwith the Federal Reserve and the Federal Reserve Bank of New York todevelop three new rates. In June 2017, the ARRC selected one of theserates, the Secured Overnight Financing Rate (SOFR), as its recommendedalternative to U.S. dollar LIBOR.

The SOFR is based on repo interest rates. A repo, or repurchaseagreement, is a secured loan; one party sells a security to anotherparty and agrees to repurchase it later at a set date and price. Becauserepos are a key source of short-term funding in the financial system, arate based on these transactions is a good candidate for an alternativereference rate. The SOFR, like the Fed Funds rate, is a one dayovernight rate and is the daily average rate for repo transactionssecured against US treasuries. It measures the rate on average tradednotional value.

The SOFR will include overnight, Treasury-backed repo transactions thattake place in the Bank of New York Mellon's triparty repo system or arecleared through one of two Fixed Income Clearing Corporation platforms:(1) the Delivery-Versus-Payment Repo Service and (2) the GeneralCollateral Finance (GCF) Repo Service.

A Bank of England working group approved SONIA as its preferredshort-term interest rate benchmark thereafter. The SONIA index tracksthe rates of actual overnight funding deals on the wholesale moneymarkets, rather than relying on submitters like the Libor benchmarkdoes. SONIA's use will minimize “opportunities for misconduct,”

As both SOFR and SONIA look at prior transactions, e.g. repotransactions or overnight funding agreements, to set their rates, theymay also be considered “backward” looking rates as discussed above.However, LIBOR, being based on projected transactions, and predeterminedat the start of a time period, may be considered a “forward” lookingrate. It may be advantageous, then, to replace LIBOR with a similarforward looking alternative.

Furthermore, while computers may be used as tools in determining LIBOR,the underlying survey methodology at its core render the process onewhich cannot be fully automated and objectively calculated and willnecessarily always comprise a bias-able and/or manipulatable underlyingcomponent necessitating significant regulation and oversight.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a computer network system, according to some embodiments.

FIG. 2 depicts a general computer system, according to some embodiments.

FIG. 3 depicts a storage data structure, according to some embodiments.

FIG. 4A depicts another storage data structure, according to someembodiments.

FIG. 4B depicts yet another storage data structure, according to someembodiments.

FIG. 5 depicts a block diagram a system for automatically determining aforward interest rate value for one or selected future time periods.

FIG. 6 depicts a flow chart showing the operation of the system of FIG.5 according to one embodiment.

FIG. 7 depicts a graph of example outputs of the system of FIGS. 5 and 6for an example time period.

FIG. 8 depicts a table describing an example definition of one and threemonth SOFR futures contracts for use with the system of FIG. 5

FIG. 9 depicts a graph of example outputs of the system of FIGS. 5 and 6for an example time period.

DETAILED DESCRIPTION

The disclosed embodiments relate to computation of a forward interestrate for a select future time period subsequent to a current date. Inparticular, the disclosed embodiments determine a current price for eachof a set of interest rate futures contracts having consecutiveexpiration months which collectively include the selected future timeperiod, and based thereon compute an implied interest rates for each ofthe consecutive expiration months. The disclosed embodiments furtheraccount for days within the select period where the interest rate mayvary for reasons other than the transaction of the set of futurescontracts, i.e. other than for reasons related to supply/demand, such asdue to governing authority policy changes. In particular, the disclosedembodiments may determine whether any of the consecutive expirationmonths include a day on which an interest may change based on an eventunrelated to transactions involving any of the set of interest ratecontracts, and for each of the expiration months determined not toinclude a day on which an interest may change based on an eventunrelated to transactions involving any of the set of interest ratecontracts, compute a baseline interest rate for each day of theexpiration period determined not to be affected by transactionsinvolving any of the set of interest rate contracts and compute anadjusted baseline interest rate for each day of the expiration perioddetermined to be affected by transactions involving any of the set ofinterest rate contracts. For each of the expiration months determined toinclude a day on which an interest may change based on an eventunrelated to transactions, starting with the expiration month nearest orfurthest away from the current date, the disclosed embodimentsiteratively extrapolate from the baseline and adjusted baseline interestrates for the days of the prior or subsequent month forward/backward dayby day to determine extrapolated interest rate values for each day ofthe expiration month from the last day thereof until the day on which aninterest may change based on an event unrelated to transactions, andderive a derived interest rate, based on the extrapolated rates and theoverall implied interest rate for the month, for each day from thebeginning of the expiration month determined to include a day on whichan interest may change based on an event unrelated to transactions up tothe day on which an interest may change based on an event unrelated totransactions. The disclosed embodiments then determine an initialinterest rate value accorded to the current day and compute the forwardinterest rate for the select future time period by combining, such as byaveraging or compounding, the initial interest rate value, baselineinterest rate value, adjusted baseline interest rate value, extrapolatedinterest rate value or derived interest rate value for each day of theselect future time period.

The disclosed embodiments enable automated determination of a stable,replicatable and risk-free short term forward reference rate which maybe predetermined at the start of a time period and which furthereliminates the inherent issues with LIBOR discussed above.

The disclosed embodiments utilize data observed or otherwise derivedfrom the trading of futures contracts having short term interest ratebased underliers, e.g. based on overnight interest rates, and, in oneembodiment, are integrated with an electronic transaction processingsystem, e.g. an electronic trading system, to access data indicative ofthe trading thereof, and therefore avoid reliance uponsubjective/opinion inputs. The disclosed embodiments enable generationof rates for varying forward terms, e.g. 1, 2, 3, 6 or 12 months usingthe observed prices of a set of suitable futures contracts, e.g. dataindicative of the symbiotic market driven negotiated estimates/consensusof capitalistic/self-motivated traders of multiple overlapping shortterm backward looking interest rate products. Inherent biases, being anecessary and visible part of any market, are eliminated via thenegotiated and transparent operation of the transaction processingsystem based on upon which the disclosed embodiments derive theautomatically determined forward short-term interest rate.

The disclosed embodiments may be operated on a daily basis using arolling set of inputs to provide continuously updated projections forthe desired terms.

As the disclosed system utilizes data collected from the trading offutures contracts, it may be implemented in conjunction with electronicdata transaction processing system which implements an electronictrading system, described below, which processes data transactionscomprising trades for futures contracts.

As will be described, in one embodiment, the prices of Fed Funds futurescontracts are utilized. Fed Funds futures contracts are a way for one,via the price they pay for the contract, to assess or hedge the futureFed Funds overnight rate, e.g. to hedge against or speculate on changesin short term interest rates. Essentially, traders buy these contractswhen rates are expected to fall and sell them when rates are expected torise and, accordingly, the price of a given Fed Funds futures contract,being the negotiated price as between the transacting parties,represents what traders think will happen to the rate in the nearfuture, not what it is right now. Generally, for contracts which expirein the current month, the price of the contract is equal to the weightedaverage of the actual fed rates realized to date and expected rates forthe remainder of the month: as the expiration/settlement dateapproaches, the contract price varies less on expected rates and isdetermined primarily by the realized rates during the month.Accordingly, FOMC actions that occur in middle of delivery month mayhave little impact on price as the price factors in rates alreadyrealized except where the FOMC may make an unexpected change in ratesand the remaining unrealized rates differ from those already realized.In contrast, a contract expiring in the month after a fed meeting morefully expresses market expectations as, for deferred expirations, theprice is based on the average expected fed rate for the expirationmonth, i.e. completely based on expected rates. Therefore, the disclosedembodiments leverage the price of Fed funds futures contracts fordifferent months so as to determine how the market expects the federalfunds rate to move over time and automatically predict the cost of moneybeyond the current date.

In one embodiment, a process for automatically computing a forwardinterest rate for given term may include converting the prices of eachof a set of futures contracts with periods (from initial trading toexpiration) spanning the desired term, such as the fed fund futurescontracts described below. For example, when the price is in the formatof 98.75, one may convert that to a yield or an interest rate bysubtracting that number from 100, i.e. 100 implies 0%, so for 100-98.75(which is the futures price) the implied rate would be 1.25% over theterm of that futures contract. Basically, the futures price and interestrate are inversely related, i.e. if the futures price goes up, the yieldor interest goes down and vice versa, and if the futures price goes over100, you will get a negative interest rate.

Next, the computed interest rate for each contract is broken down into abaseline rate and different seasonal/turn adjustment rates which areused to accord each day of the expiration month of the contract with anapplicable interest rate, e.g. if the end of the month is on a Sunday,then the Friday night, Saturday night and Sunday night overnight ratesmay all be considered to be a turn period and turn rates for those 3days out of the 30 day contract period—if the futures contract is a30-day contract, the overnight rates for 3 of those days would beadjusted to a seasonal or turn rate, while the remaining 27 days wouldbe accorded the baseline rate, wherein the combination of theturn-adjust and baseline overnights rates for all days in the periodwould result in the computed implied rate described above. Generally, aturn-date is one on which there is a regular/predictable change in therate, either up or down, from the baseline rate for that day, which thenreverts back to the baseline rate on the next non-turn date. Typically,the rate for a turn-date drops relative to the baseline rate due to adrop in demand, e.g. for money. FOMC, or other interest rate governingauthority, meeting dates, on the other hand, are not consideredturn-dates as they either result in no change to the baseline interestrate or a complete change thereto, i.e. one that does not revert back tobaseline rate thereafter.

The determination of which days of a given period would be consideredadjustable “turn-dates,” as well as the adjustment to apply to theovernight rates for those dates, may be determined in advance, such asbased on dates known to have rates which deviate from the baseline aswell as date which, based on historical data, have shown to have rateswhich deviate. For example, it is well known that the federal funds ratedrops at the end of every month by about 9-10 points. Those drops may beconsidered distortions and the disclosed methodology accounts for thosedistortions and adjusts the baseline rate based on the distortions sothat the end result is the interest rate that corresponds to the futuresprice. Other dates which may be considered known turn-dates, aside frommonth-end, may include quarterly US tax deadlines, treasury coupondates, treasury auction settlement dates, etc. With regards toadditional turn dates, other than known turn dates, for example, one maylook at historical daily overnight rates and identify those dates, suchweekend days, etc. where the rate deviated from other the rates of otherdates, in manner that indicated a significant change in supply/demandconditions so as to affect the baseline rate. For example, based onhistorical daily overnight rates, a turn-date may be defined as a dateon which the overnight rate deviates from the rate of the prior day, oran average of a defined number of preceding days, by more than athreshold percentage, such as 10%, or more than a threshold number ofstandard deviations, such as 1.

While the underlying contract term of each futures contract used by thedisclosed methodology will be monthly, the disclosed system is capableof computing a forward rate for different terms, e.g. 1 month, 3 months,6 months, 12 months, similar to the terms for which LIBOR rates are madeavailable.

The baseline rate, in combination with the turn rates accounting forseasonal irregularities in supply and demand as evidenced by deviationsin the interest rates from baseline values, creates a model for what theforward rates will be including adjustments. This allows for thecreation of a short-term forward rate, i.e., one can take the first 90days to create a 3-month benchmark and 180 days to create a 6-monthbenchmark, etc. Essentially, the disclosed embodiments utilize thebaseline and seasonality rates that are based on 1-month contracts, i.e.contracts whose settlement value is computed at the end of theexpiration month based on the underlie rates for each date of thatmonth, and then using consecutive 1-month contracts to create a productthat has varying terms such as 3-month, 6-month, etc. Note that if onewere to apply the disclosed process during the middle of a month, theprocess may require four, consecutive, one month contracts to determinea 3 month rate, seven consecutive one month contracts to determine a 6month rate, 13 consecutive one month contracts to determine a 12 monthrate, etc.

Referring now FIG. 7, there is shown an example output 700 of thedisclosed embodiments showing computed forward interest rates as of Sep.21, 2017. The orange line 702 depicts the constant overnight rate duringeach futures period and depicts jumps wherever there are futures priceschanging at the end of every month. The blue line 706 depicts thediscrete changes in the interest rate that occur on FOMC meeting datesand acts as a step/multiplier to force rate changes to occur on thosemeeting dates. Gray line 704 depicts the adjusted rate (the discreteFOMC interest rate changes (blue 706) modified to include turn dates andbaseline rates) factoring in the turn dates and their correspondingovernight rates and depict spikes downward showing the turns.

More particularly, the disclosed system/method for automaticallycomputing one or more forward short-term interest rates for a currentday may include the following inputs:

-   -   Current/prevailing futures prices for a set of relevant futures        contracts. In one embodiment, the selected contracts must be        liquid/readily tradeable and the periods for all of the        contracts, i.e. the time from initial trading until expiration,        should collectively cover the time period for which one wishes        to generate forward rates. For example, but not limited to, one        may utilize “30 day Federal Funds Futures”, monthly SOFR futures        (to be designed), monthly Sonia futures, monthly Eonia futures,        etc., or combinations thereof, having an initial trading date        that is within 13 months of the current date, e.g. to project        forward rate for up to the next 12 months. It will be        appreciated that “strips” of the above contracts, i.e.        consecutive contracts transacted in a single transaction, e.g. 3        month quarterly contracts, may also be used. For example, as 30        day Fed Funds futures contracts, on any given date, are        available expiring in the current month and at least next 36        months, one may select the contract expiring in the current        month and those 12 contracts expiring in each of the next        consecutive 12 months. Where strips are used, they may be        combined with available monthly contracts, used, due to their        granularity, in the front/earlier months, to cover the necessary        forward time period. In one embodiment, the current/prevailing        prices of each contract, which are not yet expired and may still        be continued to be traded, may be determined using volume        weighted average price (VWAP), time weighted average price        (TWAP), or a combination thereof, where VWAP, a measure of the        average price at which a contract is traded over the trading        horizon, is the ratio of the value traded to total volume traded        over a particular time horizon (which is implementation        dependent and in some implementations may be as little as 15        minutes), and TWAP is the average price of a security over a        specified time. It will be appreciated that other        methodologies/mechanisms for determining prevailing futures        contract prices may be utilized, alone or in combination, such        as the end of day settlement procedures of the electronic        trading system, a market snapshot (derived from executing the        settlement procedures at another time other than the settlement        time), or other method/mechanism now available or later        developed, or a combination thereof.    -   Identification of dates requiring adjustment for seasonality or        “turn” and the Seasonal or Month-end Turn adjustment value for        those dates: For example, known turn dates include month-end        whereas other turn dates may be identified based on historical        data. The adjustment values for all of the turn dates may then        be determined based on historical data. For example, if one        defines the lookback period as 1 year, known turns always occur        at month ends, e.g. they run between business dates, thus begin        on the last good business day of a month and end on the first        good business day of the following month. By reviewing        historical data, additional turn dates may be identified. For        example, for each month end in the preceding 12 months one looks        at daily observed rates for the turn period as defined here and        compare with the average of the day previous to the turn and the        day following the turn. The difference is defined as the average        for that period. Finally, the average over the 12 observations        is computed.    -   Starting point value: this is the first data point in the model        used by the disclosed embodiments and is the interest rate        applied to the period between the current date (today) when the        forward rates are being determined and the next day (tomorrow).    -   Start and End dates of each of the futures contracts in the        relevant set: the initial trading and settlement/expiration        dates of each futures contract in the set being relied upon.

The disclosed system/method may then operate as follows:

-   -   The current/prevailing futures prices of each contract in the        set being relied upon is converted to an implied interest rate        for the expiration period of the contract by, for example,        subtracting the price (computed using VWAP and/or TWAP) from 1        (or 100). As noted above, the interest rate for a fed funds        futures contract will be the overall rate for the expiration        month of the particular contract determined at the end of that        month (the aggregate of the fed funds overnight rate for each        day of the month). Different futures contracts may be used which        have different period over which the settlement interest rate is        determined. Prior to expiration, the expected or implied        interest rate for the expiration may be derived from the current        price.    -   Each implied interest rate is then broken down into a baseline        rate and a turn/seasonal adjusted rate as follows:        -   Total number of days (D_(T)) in the expiration month;        -   Number of adjustable, e.g. “turn,” days (D_(a)). Known and            additional turn days are defined in advance based on            historical data, e.g. the last 1 or 2 days of a month, as            well as other days, where supply/demand conditions may            affect the baseline rate.        -   Interest Rate projected at contract expiration implied by            future (R_(f)). As described above, this represents the            total interest rate returned or expected to be returned.            This is combination of a baseline rate occurring on normal            days of the expiration period and turn adjusted rates            occurring on defined turn days of the expiration period.            This interest rate, calculated/implied from the price as            described above, represents a constraint in that during any            futures period, i.e. the time between being initially            offered for trading and the expiration, the combination of            baseline rates and seasonal/turn adjusted dates must return            the overall rate implied by the price of the future (whether            by linear average or compounded average in the case a future            is so defined).        -   Turn adjustment (A) defined in advance based on historical            data, e.g. derived from an average based on a periodic look            back of previous instances. For example, if the turn            adjustment is negative (in a rate sense) i.e., making the            interest rate lower on turn days then the baseline must be            higher to compensate and return the all-in rate implied by            the futures price.        -   Solving for Baseline rate (R_(b)) and Turn adjusted rate            (R_(a)):            R _(f)=[D _(a) ·R _(a)+(D _(T) −D _(a))·R _(b)]/D _(T)        -   And therefore:            Baseline Rate R _(b)=[R _(f) ·D _(T) −D _(a) ·A]/D _(T)            Turn-Adjusted Rate R _(a) =R _(b) +A

Once completed for all 13 contracts, a set of 13 R_(a)/R_(b) values isestablished along with the non-adjustable and turn dates for allcalendar days of the 13 month window. In one embodiment, the system mayexcept as inputs, the baseline rate R_(b) and the turn adjustment A tobe applied thereto, rather than the computed turn adjusted rate R_(a).

The starting point rate for the current month, i.e., the period of timefrom, but not including, the current date to the end of the currentmonth, is determined by aggregating, e.g. averaging, the known fed fundseffective overnight rates from the beginning of the month up to thecurrent date. The rates known to-date essentially define the daily ratesleft to be determined for the current month. These known rates arefactored into the overall baseline rate R_(b) and turn adjusted rateR_(a) for the current month, i.e. knowing the implied rate R_(f) basedon the futures contract price for the month, along with the average ofthe known interest rates to date, the implied interest rates for theremaining days of the month may be computed knowing that the overallaverage must equal R_(f). The derived rate for the remaining days of themonth is used as the R_(b) value and adjusted for any turn date withinthose remaining days.

Baseline rates R_(b) and turn adjusted rates R_(a) are then accorded toeach calendar day of the remainder of the current month and to eachcalendar day of the upcoming 12 months according to the followingprocess.

First, as noted above, a baseline starting point rate is determined forthe current day (as opposed to the rest of the remaining days of themonth for which the R_(b) was calculated as described above) byconsidering the prior day's, i.e. yesterday's, fed funds overnight ratewhich provides a starting point for today's rate. It is also determinedwhether or not today is a turn date. If today is a turn date, a turnadjustment A is applied to the prior days rate to adjust for the turn.This rate, turn adjusted if necessary, then acts as a starting point onthe curve.

Next, it is determined which of the current month and the subsequent 12months do or do not contain a central bank or other monetary policysetting administration/committee, e.g. FOMC, meeting date. At thesemeetings, the participants determine whether or not to effect a changein policy affecting overnight rates. Accordingly, it is at these timesthat interest rates may or may not change, in manner unrelated tosupply/demand, i.e. unrelated to the transactions of the futurescontracts, i.e. prices, covering the period, potentially creatingdiscontinuities in the rates prior and subsequent to these meetingswhich are determined based on the futures contracts transactions, i.e.prices, as described. As meeting dates are dates on which the rates canchange, but may not, based on events other than the transactions of therequisite futures contacts, they are not treated the same as turn dateswhere, based historical data related to past transactions, the rates dochange.

For example, the FOMC currently has 8 meeting scheduled per year thusthere are months that do not contain meetings. The schedule for 2017,for example, was: January 30-31, March 20-21, May 1-2, June 12-13,July/August 31-1, September 25-26, November 7-8 and December 18-19.Announcements are made on the second day of meetings and this istherefore the pertinent single day, thus the July/August meeting aboveis deemed to occur in August with July then being deemed to be anon-meeting month. More obviously, in addition to July, there are nomeetings in February, April and October. It will be appreciated that themeeting dates, and therefore which months are determined to be meetingmonths and non-meeting months, may vary year to year. Further, in anygiven year there may be more or fewer meetings. Finally, depending uponthe set of instruments being utilized by the disclosed embodiments, thedesignation of what constitutes a meeting may vary.

For each non-meeting month, the baseline rate R_(b) and turn adjustedrate R_(a) calculated for the particular contract expiring in that monthare accorded to the non-adjustable and turn days respectively.

In the disclosed embodiments, the interest rate model is constrainedsuch that any jumps in interest rates occur discretely on the meetingdates. In other words, when considering the baseline rates R_(b) andturn adjusted rates R_(a) accorded to the days in the month preceding ameeting month and the month subsequent to that meeting month, anychange, e.g. discontinuity, there between will be deemed to occur on themeeting date.

In particular, with a starting point forward rate and the forward ratesfor all days of each non-meeting month defined, this leaves 8 months forwhich to compute the forward rates. For these 8 meeting-months, thereare days before the meeting date and days after the meeting date forwhich a rate must be accorded.

Generally, starting with a non-meeting month, the baseline rate for thatmonth is extrapolated forward into the subsequent meeting month up tothe meeting date, and/or backward into the prior meeting month up to themeeting date. With the extrapolated rates accorded then to the days ofthe meeting month on one side of the meeting date, the rate accorded toeach day on the other side of the meeting date is projected using theimplied rate from the futures price for the month. In particular, usingthe baseline rate R_(b) and turn adjusted rate R_(a) for the non-meetingmonth immediately preceding or following a meeting month, those ratesare extrapolated either forward or backward, from the end of thesubsequent, or beginning of the prior, non-meeting month at that month'ssame rate, up or back to the meeting date within the meeting month so asto fill in the front or back half of the meeting month. In order toaccord rates to the days of the remaining half of the meeting monthfollowing or preceding the meeting date, the effective implied rate forthe entire meeting month is calculated based on the rates applied todays of the remaining half and the computed implied interest rate of thefutures contract expiring in that month, e.g. the aggregate of the ratesaccorded to all of the days of the month. As the aggregate of theaccorded rates for each day of the month should equal the computedimplied interest rate for the futures contract expiring that month, ifthe implied rate is known along with the accorded rates of one half ofthe month, the accorded rates for the days of the remaining half of themonth may be computed therefrom. The rates for the all of the days ofthe 8 meeting months are then similarly determined via the solution of aset of similarly determined equations, e.g. a closed form solution to aquadratic program. In this manner rate changes, i.e. actual changes inthe rate as opposed to turn-based aberrations, are constrained to occuron the meeting date of the meeting date months.

With the daily forward rates accorded to every calendar day forward ofthe current out to 13 months, term rates for desired forward terms maybe computed. For example, to determine the 1, 2, 3, 6 or 12 month termrates, one may average, or, alternatively, compound, the daily rates forthe desired term.

Each day until the subsequent month is reached, the above computationsmay be repeated with the then current prices of the same set of futurescontracts, wherein once the next month is reached, the set of futurescontracts is adjusted to drop the oldest month and add the month that isnow 13 months out to maintain 13 months of coverage, i.e. the set offutures contracts is rolled forward on a monthly basis to continuouslycover the future time period(s) for which forward rates are to becomputed. In this way, the disclosed embodiments produce a continuousstream of forward term rates.

The disclosed embodiments may operate automatically to compute forwardinterest rate value for selected time periods based on the overlappingconsecutive expiration periods of substantially continuously tradedinterest rate futures contracts which objectively capture unbiasedexpectations adjusted to account for known anomalies. As opposed tomerely defining rates for all time periods between FOMC meeting dates,with adjustments to minimize change of rate other than at FOMC meetings,the disclosed embodiments, while similarly constraining overnight ratesto being constant between FOMC meetings, first declare rates in monthsthat do not have meetings and then extrapolates forwards and backwardsor bootstraps to fill in the gaps. This disclosed embodimentadditionally implements turn adjustments in order to better representthe actual market and mitigate against the possibility of an undesirablesolution (where the calculated overnight rates jump excessively).Accordingly, the disclosed embodiments need not an adjustment functionor use of a quadratic program solver.

The disclosed technology addresses the need in the art for a systemwhich can operate automatically without subjective inputs. Specifically,the disclosed technology solves a problem that uniquely arises in thefields of computer technology and exchange computing systems, whereforward values need to be computed based on objectively obtained datarather than manually generated subjective/opinion data. For example, byinterfacing with an electronic trading system and utilizing dataindicative of the results of the trading of particular futurescontracts, the system may determine objective expected interest ratevalues determined via the negotiations between self-interested partiesand utilize this data to generate an interest rate model for an overallfuture time period from which interest rate values for particularsub-periods may be derived. Thus, the disclosed solution is rooted incomputer technology in order to overcome a problem specifically arisingin the computer systems used by electronic trading systems. Indeed, thesubject technology improves the functioning of the computer by allowingit to automatically generate data it could not generate previously.

The disclosed embodiments are drawn to systems and methods that includespecific computing components, each being specially programmed toperform a technological function as part of a greater technologicalprocess. The disclosed embodiments include separate system componentsinterconnected in a specific way to implement aspects of the disclosedsystem and include sufficient specific structure and function and, assuch, are not drawn to an abstract idea.

The disclosed embodiments are not directed to any method for “obtaining,transforming and determining,” which is involved in all computingfunctionality. The disclosed embodiments and features recited in thisregard provide numerous advantages. The instant embodiments do notpreempt all methods of “obtaining, transforming, and determining,” andare specifically directed towards the disclosed functionality. Thedisclosed embodiments implement specific rules and features that improvethe operation of a particular genus of a technological process, whichdoes not preempt all techniques of obtaining, transforming anddetermining, which, at some level, is part of every computing process.

The disclosed embodiments may be implemented in an electronic datatransaction processing system, as described below, that processes dataitems or objects, such as an exchange computing system as described inmore detail below. Customer or user devices (e.g., client computers) maysubmit electronic data transaction request messages, e.g., inboundmessages, to the data transaction processing system over a datacommunication network. The electronic data transaction request messagesmay include, for example, transaction matching parameters, such asinstructions and/or values, for processing the data transaction requestmessages within the data transaction processing system. The instructionsmay be to perform transactions, e.g., buy or sell a quantity of aproduct at a range of values defined by equations. Products, e.g.,financial instruments, or order books representing the state of anelectronic marketplace for a product, may be represented as data objectswithin the exchange computing system. The instructions may also beconditional, e.g., buy or sell a quantity of a product at a given valueif a trade for the product is executed at some other reference value.The data transaction processing system may include various specificallyconfigured matching processors that match, e.g., automatically,electronic data transaction request messages for the same one of thedata items or objects. The specifically configured matching processorsmay match, or attempt to match, electronic data transaction requestmessages based on multiple transaction matching parameters from thedifferent client computers. The specifically configured matchingprocessors may additionally generate information indicative of a stateof an environment (e.g., the state of the order book) based on theprocessing, and report this information to data recipient computingsystems via outbound messages published via one or more data feeds.

The application may be executed by one or more of the matchingprocessors. Thus, the application may be a software match engine module,such as the match engine module illustrated in FIGS. 4A and 4B, whichincludes multiple different stages, e.g., the conversion component 402,match component 406, and publish component 410.

Futures Exchange

A Futures Exchange, referred to herein also as an “Exchange”, such asthe Chicago Mercantile Exchange Inc. (CME), utilizing an ExchangeComputing System, described in detail below, provides an electronic dataprocessing system which implements a contract market where futures andoptions on futures are traded. Futures is a term used to designate allcontracts for the purchase or sale of financial instruments or physicalcommodities for future delivery or cash settlement on a commodityfutures exchange. A futures contract is a legally binding agreement tobuy or sell a commodity or other underlier, such as a financialinstrument, at a specified price at a predetermined future time. Anoption is the right, but not the obligation, to sell or buy theunderlying instrument (in this case, a futures contract) at a specifiedprice within a specified time. The commodity or instrument to bedelivered in fulfillment of the contract, or alternatively thecommodity/instrument for which the cash market price shall determine thefinal settlement price of the futures contract, is known as thecontract's underlying reference or “underlier.” The terms and conditionsof each futures contract are standardized as to the specification of thecontract's underlying reference, the quality of such underlier,quantity, delivery date, and means of contract settlement. CashSettlement is a method of settling a futures contract whereby theparties effect final settlement when the contract expires bypaying/receiving the loss/gain related to the contract in cash, ratherthan by effecting physical sale and purchase of the underlying referenceat a price determined by the futures contract price.

In particular all futures contracts may be characterized by at least adate of first availability, i.e. the first day on which the contract maybe traded, and an expiration date when the contract will be settled. Thetime between the first availability and the expiration may be referredto as the contract life or lifespan.

An interest rate futures contract, also referred to as an interest ratefuture, is a futures contract having an underlying instrument/asset thatpays interest, for which the parties to the contract are a buyer and aseller agreeing to the future delivery of the interest bearing asset, ora contractually specified substitute. Such a futures contract permits abuyer and seller to lock in the price, or in more general terms theinterest rate exposure, of the interest-bearing asset for a future date.One example of an interest rate futures contract is a short terminterest rate (“STIR”) contract. Examples of such short-term interestrate (“STIR”) futures include CBOT 30-Day Fed Funds futures, CME 1-MonthEurodollar futures, 3-Month Eurodollar futures, or 3-Month OvernightInterest Rate Swap (“OIS”) futures, and NYSE Liffe Eonia futures, EoniaSwap Index futures, or Short Sterling futures. As described above, theseare backwards looking futures contracts as opposed to futures contractsbased on forward looking rate, e.g. Eurodollar futures based on Libor.

With regards to futures contracts based on backwards looking rates, e.g.Fed Funds Futures, in addition to being characterized by a firstavailability and an expiration, interest rate futures may be furthercharacterized by a duration over which the subject interest rate will becalculated, e.g. averaged, for the purpose of determining the finalsettlement price. Typical durations are monthly and quarterly. So for amonthly interest rate contract, average interest rate during theexpiration month will be calculated to determine the final settlementprice for that contract and for a quarterly contract, the averageinterest rate for the quarter at the end of which the contract expires,will be calculated to determine the final settlement price, etc.

One purpose of interest rate futures contracts may be to achievesynthetic interest rate swap exposure through use of interest ratefutures that directly reference the short-term interest rate that servesas the floating rate benchmark for the interest rate swap. The marketparticipant may construct a futures proxy for the desired interest rateswap exposure with strips of such futures, i.e., sequences of STIRfutures contracts with consecutive delivery months. For example, amarket participant might use CME 3-month Eurodollar futures in each ofthe nearest 40 quarterly delivery months, in varying quantities, tosynthesize a 10-year US dollar interest rate swap. Similarly, varyingamounts of CBOT 30-Day federal funds futures for each of the nearest 12monthly delivery months might be combined to create a synthetic proxyfor a 1-year US dollar overnight index swap.

Typically, the Exchange provides for a “clearing house” through whichall trades made must be confirmed, matched, and settled each day untiloffset, i.e. closed or otherwise nullified by an opposing position, ordelivered. The clearing house is an adjunct to the Exchange, and may bean operating division of the Exchange, which is responsible for settlingtrading accounts, clearing trades, collecting and maintainingperformance bond funds, regulating delivery, and reporting trading data.The essential role of the clearing house is to mitigate credit risk.Clearing is the procedure through which the Clearing House becomes buyerto each seller of a futures contract, and seller to each buyer andassumes responsibility for protecting buyers and sellers from financialloss due to breach of contract, by assuring performance on eachcontract. A clearing member is a firm qualified to clear trades throughthe Clearing House.

As an intermediary, the Exchange bears a certain amount of risk in eachtransaction that takes place. To that end, risk management mechanismsprotect the Exchange via the Clearing House. The Clearing Houseestablishes performance bonds (margins) for all Exchange products andestablishes minimum performance bond requirements for customers ofExchange products. Performance bonds, also referred to as margins, arethe funds that must be deposited by a customer with his or her broker,by a broker with a clearing member, or by a clearing member with theClearing House, for the purpose of insuring the broker or Clearing Houseagainst loss due to breach of contract on open futures or optionscontracts. Performance bond is not a part payment on a purchase. Rather,performance bond helps to ensure the financial integrity of brokers,clearing members, and the Exchange. The Performance Bond to ClearingHouse refers to the minimum dollar deposit which is required by theClearing House from clearing members in accordance with their positions.The initial margin is the total amount of margin per contract requiredby the broker when a futures position is opened. Maintenance, ormaintenance margin, refers to a minimum amount, usually smaller than theinitial performance bond, which must remain on deposit in the customer'saccount for any position at all times. A drop in funds below themaintenance margin level requires a deposit back to the initial marginlevel, i.e., a performance bond call. If a customer's equity in anyfutures position drops to or under the maintenance level because ofadverse price action, the broker must issue a performance bond/margincall to restore the customer's equity. A performance bond call, alsoreferred to as a margin call, is a demand for additional funds to bringthe customer's account back up to the initial performance bond levelwhenever adverse price movements cause the account to go below themaintenance margin level. Within any given Exchange trading day, afutures contract position that is newly entered and cleared, and thenheld through the end of the trading day, is marked-to-market by theExchange, i.e. the current market value, as opposed to the book value,is determined, from the trade price at which the contract position wasentered to the trading day's end-of-day settlement price for thecontract. Similarly, a futures contract position that is extant at thebeginning of a given Exchange trading day, and that is then held throughthe end of the trading day, is marked-to-market by the Exchange from theprevious day's end-of-day settlement price to the current day'send-of-day settlement price for the contract. In both cases, the net ofthese amounts is banked in cash. That is, if the mark-to-market recordsan increase in the price of the contract, the Clearing House credits tothose clearing members holding open long positions, and debits fromthose clearing members holding open short positions, the pecuniary valueof the change in contract price. If the mark-to-market records adecrease in the price of the contract, the Clearing House debits thoseclearing members holding open long positions, and credits to thoseclearing members holding open short positions, the pecuniary value ofthe change in contract price. Such credits to, or debits from, clearingmember accounts at the Clearing House are typically referred to as“variation margin.”

Multiple futures contracts for a given underlier may be issued, orotherwise made available for sale/trading, having various futureexpiration dates, such as monthly or quarterly expiration dates. Forexample, various futures contracts for oil, gas, treasury securities,etc. may be issued, each having a different expiration. For example, forgiven underlier, contracts expiring each month of a future calendar yearmay be available for trading today. These contracts may be madeavailable for trading close to or well in advance of their particularexpiration date. For example, a contract having a January 2019expiration date may be made available for trading in January of 2017.The time between issuance and expiration may be months or even years. Atthe time a trader takes a position in a particular contract for givenunderlier, e.g. buys a futures contract to buy or sell the underlier inthe future, the nearest expiring available futures contract for thatparticular underlier is referred to as the “front month” contract. Alllater expiring contracts for that underlier are referred to as “backmonth” contracts.

Particularly, with respect to interest rate futures contracts, such asSTIR contracts, different traders may wish to trade contracts for manydifferent time periods, e.g. to gain or hedge exposure to futureinterest rate changes, and for different durations over which thesubject interest rate is calculated, e.g. monthly quarterly etc.Depending on the needs of the of the trader, e.g. their tradingstrategy, a given trader may desire to trade front month, back month ora combination of those contracts, and different durations, e.g. monthlyquarterly, etc. e.g. for the purpose of hedging or mitigating risk, etc.

Accordingly, the Exchange may offer many different contract variationsin order to meet these trader needs. Of course, it may not be feasibleto offer every possible expiry/duration permutation of every contract.This limitation, coupled with a trader's typical desire to minimize thenumber of transactions and the number of positions, results in Exchangesoffering select subset of contracts for trading at any given point intime. Typically, shorter duration contracts are offered with expirationsoccurring in the near future while quarterly duration contracts areoffered with expirations occurring further out. This reflects thedecreasing need for precision the further into the future one looks. Forexample, an exchange may offer monthly duration interest rate contractswith expirations in the following 6-12 months while quarterly durationcontracts may be offered with expirations 12-36 months out.

For example, the 30-Day Fed Funds futures and options contracts areimportant risk management tools for anyone who wants to hedge against orspeculate on changes in short-term interest rates brought about bychanges in Federal Reserve monetary policy. Fed Funds futures providetrading opportunities and hedging resources for the management of riskexposures associated with a variety of money market interest rates.Standard and mid-curve options on Fed Funds futures offer marketparticipants instruments with defined risk parameters that can be usedto express a view on the likelihood of Fed policy changes. Together,these products serve a wide spectrum of users and uses.

The Fed Funds futures contract price represents the market opinion ofthe average daily fed funds effective rate as calculated and reported bythe Federal Reserve Bank of New York for a given calendar month. It isdesigned to capture the market's need for an instrument that reflectsFederal Reserve monetary policy. Because the Fed Funds futures contractis based on the daily fed funds effective rate for a given month, ittends to be highly correlated with other short-term interest rates andis useful for managing the risk associated with changing credit costsfor virtually any short-term cash instrument. Fed Funds futures can beused either speculatively to anticipate changes in monetary policy ormore conservatively to hedge inventory financing risk across manydifferent markets.

Another interest rate futures contract which may be offered is one basedon the above described SOFR, and which may be offered and administeredsimilar to the Fed Funds contracts, e.g. expiring 4 to 7 months out.Furthermore, strips of SOFR contracts, i.e. consecutive SOFR contractstransacted together, e.g. 3 month or quarterly, may be offered. Forexample, 3 month International Monetary Market (“IMM”) compoundedcontracts may be offered which settle at the end of the quarter, with 16quarterly contracts offered covering 4 years.

For example, as shown in FIG. 8, One and Three Month SOFR Futures may bedefined as follows:

Three-Month SOFR futures

-   -   Price is IMM Index=100 minus Rate.    -   “Rate” is business-day-compounded SOFR interest during the        contract Reference Quarter.    -   Contract Reference Quarter starts on IMM Wednesday of third        month before contract delivery month, and ends immediately        before IMM Wednesday of contract delivery month.    -   “Contract Month” is the month in which Reference Quarter begins.        Example: For a “March” contract, Reference Quarter starts on IMM        Wednesday of March and ends with contract final settlement on        IMM Wednesday of June, the contract delivery month.    -   Each basis point of contract interest is worth $25. Contract        size=$2,500×IMM Index.    -   Initial contract listings will comprise the 20 March Quarterly        months starting with June 2018 (i.e., the contract scheduled for        final settlement on Wednesday, 19 Sep. 2018)    -   Intermarket spreads versus the nearby 20 Three-Month Eurodollar        (GE) futures—quarterly, White year through Gold year—should        furnish a clear view of market assessment of the term structure        of basis spreads between 3-month SOFR OIS exposures and        corresponding 3-month Eurodollar exposures.

One-Month SOFR futures

-   -   Price is IMM Index=100 minus Rate.    -   “Rate” is arithmetic average of daily SOFR values during        contract delivery month.    -   Each basis point of contract interest is worth $41.67. Contract        size=$4,167×IMM Index.    -   Initial contract listings will comprise the nearest 7 calendar        months, starting with May 2018.    -   Intermarket spreads versus the nearest 7 monthly CBOT 30-Day        Federal Funds futures should provide timely indication of market        expectations across the nearby term structure of the fed        funds-SOFR basis spread.

Complementarity between Three-Month SOFR futures and One-Month SOFRfutures: For any Three-Month SOFR futures contract prior to the start ofits Reference Quarter, the contract rate—the “Rate” portion of the “100minus Rate” contract price—gauges market expectation of business-daycompounded SOFR during the Reference Quarter, expressed as an interestrate per annum. After the nearby contract enters its Reference Quarter,the contract rate becomes a mix of (i) known SOFR values, ie, publishedvalues for all days from start of the Reference Quarter to the present,and (ii) market expectations of SOFR values for all remaining days inthe Reference Quarter that lie ahead. As the expiring contractprogresses through its Reference Quarter, the forward-lookingexpectational component of its price plays a steadily diminishing rolein fair valuation of the contract. In general, progressively decreasinguncertainty about the contract's final settlement price means steadilyless contract price volatility. Seen in this light, the One-Month SOFRfutures strip will make a useful complement to Three-Month SOFR futuresfor market participants who seek finer granularity in framing marketexpectations of future SOFR values, or who seek finer resolution of SOFRvolatility, over the nearby 1-month to 4-month interval.

Exchange Computing System

For example, one exemplary environment where the disclosed embodimentsmay be desirable is in financial markets, and in particular, electronicfinancial exchanges, such as a futures exchange, such as the ChicagoMercantile Exchange Inc. (CME).

As described above, financial instrument trading system, such as afutures exchange, such as the Chicago Mercantile Exchange Inc. (CME),provides a contract market where financial instruments, e.g., futuresand options on futures, are traded using electronic systems. “Futures”is a term used to designate all contracts for the purchase or sale offinancial instruments or physical commodities for future delivery orcash settlement on a commodity futures exchange. A futures contract is alegally binding agreement to buy or sell a commodity at a specifiedprice at a predetermined future time. An option contract is the right,but not the obligation, to sell or buy the underlying instrument (inthis case, a futures contract) at a specified price on or before acertain expiration date. An option contract offers an opportunity totake advantage of futures price moves without actually having a futuresposition. The commodity to be delivered in fulfillment of the contract,or alternatively the commodity for which the cash market price shalldetermine the final settlement price of the futures contract, is knownas the contract's underlying reference or “underlier.” The underlying orunderlier for an options contract is the corresponding futures contractthat is purchased or sold upon the exercise of the option.

The terms and conditions of each futures contract are standardized as tothe specification of the contract's underlying reference commodity, thequality of such commodity, quantity, delivery date, and means ofcontract settlement. Cash settlement is a method of settling a futurescontract whereby the parties effect final settlement when the contractexpires by paying/receiving the loss/gain related to the contract incash, rather than by effecting physical sale and purchase of theunderlying reference commodity at a price determined by the futurescontract, price. Options and futures may be based on more generalizedmarket indicators, such as stock indices, interest rates, futurescontracts and other derivatives.

Electronic Trading

Electronic trading of financial instruments, such as futures contracts,is conducted by market participants sending orders, such as to buy orsell one or more futures contracts, in electronic form to the exchange,i.e. to the electronic data processing system which implements theelectronic trading system. These electronically submitted orders to buyand sell are then matched, if possible, by the exchange, i.e., by theexchange's matching engine, to execute a trade. Outstanding (unmatched,wholly unsatisfied/unfilled or partially satisfied/filled) orders aremaintained in one or more data structures or databases referred to as“order books,” such orders being referred to as “resting,” and madevisible, i.e., their availability for trading is advertised, to themarket participants through electronic notifications/broadcasts,referred to as market data feeds. An order book is typically maintainedfor each product, e.g., instrument, traded on the electronic tradingsystem and generally defines or otherwise represents the state of themarket for that product, i.e., the current prices at which the marketparticipants are willing buy or sell that product. As such, as usedherein, an order book for a product may also be referred to as a marketfor that product.

Upon receipt of an incoming order to trade in a particular financialinstrument, whether for a single-component financial instrument, e.g., asingle futures contract, or for a multiple-component financialinstrument, e.g., a combination contract such as a spread contract, amatch engine, as described herein, will attempt to identify a previouslyreceived but unsatisfied order counter thereto, i.e., for the oppositetransaction (buy or sell) in the same financial instrument at the sameor better price (but not necessarily for the same quantity unless, forexample, either order specifies a condition that it must be entirelyfilled or not at all).

Previously received but unsatisfied orders, i.e., orders which eitherdid not match with a counter order when they were received or theirquantity was only partially satisfied, referred to as a partial fill,are maintained by the electronic trading system in an order bookdatabase/data structure to await the subsequent arrival of matchingorders or the occurrence of other conditions which may cause the orderto be modified or otherwise removed from the order book.

If the match engine identifies one or more suitable previously receivedbut unsatisfied counter orders, they, and the incoming order, arematched to execute a trade there between to at least partially satisfythe quantities of one or both the incoming order or the identifiedorders. If there remains any residual unsatisfied quantity of theidentified one or more orders, those orders are left on the order bookwith their remaining quantity to await a subsequent suitable counterorder, i.e., to rest. If the match engine does not identify a suitablepreviously received but unsatisfied counter order, or the one or moreidentified suitable previously received but unsatisfied counter ordersare for a lesser quantity than the incoming order, the incoming order isplaced on the order book, referred to as “resting”, with original orremaining unsatisfied quantity, to await a subsequently receivedsuitable order counter thereto. The match engine then generates matchevent data reflecting the result of this matching process. Othercomponents of the electronic trading system, as will be described, thengenerate the respective order acknowledgment and market data messagesand transmit those messages to the market participants.

Matching, which is a function typically performed by the exchange, is aprocess, for a given order which specifies a desire to buy or sell aquantity of a particular instrument at a particular price, ofseeking/identifying one or more wholly or partially, with respect toquantity, satisfying counter orders thereto, e.g., a sell counter to anorder to buy, or vice versa, for the same instrument at the same, orsometimes better, price (but not necessarily the same quantity), whichare then paired for execution to complete a trade between the respectivemarket participants (via the exchange) and at least partially satisfythe desired quantity of one or both of the order and/or the counterorder, with any residual unsatisfied quantity left to await anothersuitable counter order, referred to as “resting.” A match event mayoccur, for example, when an aggressing order matches with a restingorder. In one embodiment, two orders match because one order includesinstructions for or specifies buying a quantity of a particularinstrument at a particular price, and the other order includesinstructions for or specifies selling a (different or same) quantity ofthe instrument at a same or better price. It should be appreciated thatperforming an instruction associated with a message may includeattempting to perform the instruction. Whether or not an exchangecomputing system is able to successfully perform an instruction maydepend on the state of the electronic marketplace.

While the disclosed embodiments will be described with respect to aproduct by product or market by market implementation, e.g. implementedfor each market/order book, it will be appreciated that the disclosedembodiments may be implemented so as to apply across markets formultiple products traded on one or more electronic trading systems, suchas by monitoring an aggregate, correlated or other derivation of therelevant indicative parameters as described herein.

While the disclosed embodiments may be discussed in relation to futuresand/or options on futures trading, it should be appreciated that thedisclosed embodiments may be applicable to any equity, fixed incomesecurity, currency, commodity, options or futures trading system ormarket now available or later developed. It may be appreciated that atrading environment, such as a futures exchange as described herein,implements one or more economic markets where rights and obligations maybe traded. As such, a trading environment may be characterized by a needto maintain market integrity, transparency, predictability,fair/equitable access and participant expectations with respect thereto.In addition, it may be appreciated that electronic trading systemsfurther impose additional expectations and demands by marketparticipants as to transaction processing speed, latency, capacity andresponse time, while creating additional complexities relating thereto.Accordingly, as will be described, the disclosed embodiments may furtherinclude functionality to ensure that the expectations of marketparticipants are met, e.g., that transactional integrity and predictablesystem responses are maintained.

Financial instrument trading systems allow traders to submit orders andreceive confirmations, market data, and other information electronicallyvia electronic messages exchanged using a network. Electronic tradingsystems ideally attempt to offer a more efficient, fair and balancedmarket where market prices reflect a true consensus of the value oftraded products among the market participants, where the intentional orunintentional influence of any one market participant is minimized ifnot eliminated, and where unfair or inequitable advantages with respectto information access are minimized if not eliminated.

Electronic marketplaces attempt to achieve these goals by usingelectronic messages to communicate actions and related data of theelectronic marketplace between market participants, clearing firms,clearing houses, and other parties. The messages can be received usingan electronic trading system, wherein an action or transactionassociated with the messages may be executed. For example, the messagemay contain information relating to an order to buy or sell a product ina particular electronic marketplace, and the action associated with themessage may indicate that the order is to be placed in the electronicmarketplace such that other orders which were previously placed maypotentially be matched to the order of the received message. Thus theelectronic marketplace may conduct market activities through electronicsystems.

An exchange may provide for a centralized “clearing house” through whichtrades made must be confirmed, matched, and settled each day untiloffset or delivered. The clearing house may be an adjunct to anexchange, and may be an operating division of an exchange, which isresponsible for settling trading accounts, clearing trades, collectingand maintaining performance bond funds, regulating delivery, andreporting trading data. One of the roles of the clearing house is tomitigate credit risk. Clearing is the procedure through which theclearing house becomes buyer to each seller of a futures contract, andseller to each buyer, also referred to as a novation, and assumesresponsibility for protecting buyers and sellers from financial loss dueto breach of contract, by assuring performance on each contract. Aclearing member is a firm qualified to clear trades through the clearinghouse.

An exchange computing system may operate under a central counterpartymodel, where the exchange acts as an intermediary between marketparticipants for the transaction of financial instruments. Inparticular, the exchange computing system novates itself into thetransactions between the market participants, i.e., splits a giventransaction between the parties into two separate transactions where theexchange computing system substitutes itself as the counterparty to eachof the parties for that part of the transaction, sometimes referred toas a novation. In this way, the exchange computing system acts as aguarantor and central counterparty and there is no need for the marketparticipants to disclose their identities to each other, or subjectthemselves to credit or other investigations by a potentialcounterparty. For example, the exchange computing system insulates onemarket participant from the default by another market participant.Market participants need only meet the requirements of the exchangecomputing system. Anonymity among the market participants encourages amore liquid market environment as there are lower barriers toparticipation. The exchange computing system can accordingly offerbenefits such as centralized and anonymous matching and clearing.

Electronic Data Transaction Request Messages

As used herein, a financial message, or an electronic message, refersboth to messages communicated by market participants to an electronictrading or market system and vice versa. The messages may becommunicated using packeting or other techniques operable to communicateinformation between systems and system components. Some messages may beassociated with actions to be taken in the electronic trading or marketsystem. In particular, in one embodiment, upon receipt of a request, atoken is allocated and included in a TCP shallow acknowledgmenttransmission sent back to the participant acknowledging receipt of therequest. It should be appreciated that while this shallow acknowledgmentis, in some sense, a response to the request, it does not confirm theprocessing of an order included in the request. The participant, i.e.,their device, then sends back a TCP acknowledgment which acknowledgesreceipt of the shallow acknowledgment and token.

Financial messages communicated to the electronic trading system, alsoreferred to as “inbound” messages, may include associated actions thatcharacterize the messages, such as trader orders, order modifications,order cancellations and the like, as well as other message types.Inbound messages may be sent from client devices associated with marketparticipants, or their representatives, e.g., trade order messages,etc., to an electronic trading or market system. For example, a marketparticipant may submit an electronic message to the electronic tradingsystem that includes an associated specific action to be undertaken bythe electronic trading system, such as entering a new trade order intothe market or modifying an existing order in the market. In oneembodiment, if a participant wishes to modify a previously sent request,e.g., a prior order which has not yet been processed or traded, they maysend a request message comprising a request to modify the prior request.In one exemplary embodiment, the incoming request itself, e.g., theinbound order entry, may be referred to as an iLink message. iLink is abidirectional communications/message protocol/message format implementedby the Chicago Mercantile Exchange Inc.

Financial messages communicated from the electronic trading system,referred to as “outbound” messages, may include messages responsive toinbound messages, such as confirmation messages, or other messages suchas market update messages, quote messages, and the like. Outboundmessages may be disseminated via data feeds.

Financial messages may further be categorized as having or reflecting animpact on a market or electronic marketplace, also referred to as an“order book” or “book,” for a traded product, such as a prevailing pricetherefore, number of resting orders at various price levels andquantities thereof, etc., or not having or reflecting an impact on amarket or a subset or portion thereof. In one embodiment, an electronicorder book may be understood to be an electronic collection of theoutstanding or resting orders for a financial instrument.

For example, a request to place a trade may result in a responseindicative of the trade either being matched with, or being rested on anorder book to await, a suitable counter-order. This response may includea message directed solely to the trader who submitted the order toacknowledge receipt of the order and report whether it was matched, andthe extent thereto, or rested. The response may further include amessage to all market participants reporting a change in the order bookdue to the order. This response may take the form of a report of thespecific change to the order book, e.g., an order for quantity X atprice Y was added to the book (referred to, in one embodiment, as aMarket By Order message), or may simply report the result, e.g., pricelevel Y now has orders for a total quantity of Z (where Z is the sum ofthe previous resting quantity plus quantity X of the new order). In somecases, requests may elicit a non-impacting response, such as temporallyproximate to the receipt of the request, and then cause a separatemarket-impact reflecting response at a later time. For example, a stoporder, fill or kill order (FOK), also known as an immediate or cancelorder, or other conditional request may not have an immediate marketimpacting effect, if at all, until the requisite conditions are met.

An acknowledgement or confirmation of receipt, e.g., a non-marketimpacting communication, may be sent to the trader simply confirmingthat the order was received. Upon the conditions being met and a marketimpacting result thereof occurring, a market-impacting message may betransmitted as described herein both directly back to the submittingmarket participant and to all market participants (in a Market By Price“MBP” e.g., Aggregated By Value (“ABV”) book, or Market By Order “MBO”,e.g., Per Order (“PO”) book format). It should be appreciated thatadditional conditions may be specified, such as a time or price limit,which may cause the order to be dropped or otherwise canceled and thatsuch an event may result in another non-market-impacting communicationinstead. In some implementations, market impacting communications may becommunicated separately from non-market impacting communications, suchas via a separate communications channel or feed.

For additional details and descriptions of different market data feeds,see U.S. Patent Application Publication No. 2017/0331774 A1, filed onMay 16, 2016, entitled “Systems and Methods for Consolidating MultipleFeed Data”, assigned to the assignee of the present application, theentirety of which is incorporated by reference herein and relied upon.

Clearing House

The clearing house of an exchange clears, settles and guarantees matchedtransactions in contracts occurring through the facilities of theexchange. In addition, the clearing house establishes and monitorsfinancial requirements for clearing members and conveys certain clearingprivileges in conjunction with the relevant exchange markets.

The clearing house establishes clearing level performance bonds(margins) for all products of the exchange and establishes minimumperformance bond requirements for customers of such products. Aperformance bond, also referred to as a margin requirement, correspondswith the funds that must be deposited by a customer with his or herbroker, by a broker with a clearing member or by a clearing member withthe clearing house, for the purpose of insuring the broker or clearinghouse against loss on open futures or options contracts. This is not apart payment on a purchase. The performance bond helps to ensure thefinancial integrity of brokers, clearing members and the exchange as awhole. The performance bond refers to the minimum dollar depositrequired by the clearing house from clearing members in accordance withtheir positions. Maintenance, or maintenance margin, refers to a sum,usually smaller than the initial performance bond, which must remain ondeposit in the customer's account for any position at all times. Theinitial margin is the total amount of margin per contract required bythe broker when a futures position is opened. A drop in funds below thislevel requires a deposit back to the initial margin levels, i.e., aperformance bond call. If a customer's equity in any futures positiondrops to or under the maintenance level because of adverse price action,the broker must issue a performance bond/margin call to restore thecustomer's equity. A performance bond call, also referred to as a margincall, is a demand for additional funds to bring the customer's accountback up to the initial performance bond level whenever adverse pricemovements cause the account to go below the maintenance.

The exchange derives its financial stability in large part by removingdebt obligations among market participants as they occur. This isaccomplished by determining a settlement price at the close of themarket each day for each contract and marking all open positions to thatprice, referred to as “mark to market.” Every contract is debited orcredited based on that trading session's gains or losses. As prices movefor or against a position, funds flow into and out of the tradingaccount. In the case of the CME, each business day by 6:40 a.m. Chicagotime, based on the mark-to-the-market of all open positions to theprevious trading day's settlement price, the clearing house pays to orcollects cash from each clearing member. This cash flow, known assettlement variation, is performed by CME's settlement banks based oninstructions issued by the clearing house. All payments to andcollections from clearing members are made in “same-day” funds. Inaddition to the 6:40 a.m. settlement, a daily intra-day mark-to-themarket of all open positions, including trades executed during theovernight GLOBEX®, the CME's electronic trading systems, trading sessionand the current day's trades matched before 11:15 a.m., is performedusing current prices. The resulting cash payments are made intra-day forsame day value. In times of extreme price volatility, the clearing househas the authority to perform additional intra-day mark-to-the-marketcalculations on open positions and to call for immediate payment ofsettlement variation. CME's mark-to-the-market settlement system differsfrom the settlement systems implemented by many other financial markets,including the interbank, Treasury securities, over-the-counter foreignexchange and debt, options, and equities markets, where participantsregularly assume credit exposure to each other. In those markets, thefailure of one participant can have a ripple effect on the solvency ofthe other participants. Conversely, CME's mark-to-the-market system doesnot allow losses to accumulate over time or allow a market participantthe opportunity to defer losses associated with market positions.

While the disclosed embodiments may be described in reference to theCME, it should be appreciated that these embodiments are applicable toany exchange. Such other exchanges may include a clearing house that,like the CME clearing house, clears, settles and guarantees all matchedtransactions in contracts of the exchange occurring through itsfacilities. In addition, such clearing houses establish and monitorfinancial requirements for clearing members and convey certain clearingprivileges in conjunction with the relevant exchange markets.

The disclosed embodiments are also not limited to uses by a clearinghouse or exchange for purposes of enforcing a performance bond or marginrequirement. For example, a market participant may use the disclosedembodiments in a simulation or other analysis of a portfolio. In suchcases, the settlement price may be useful as an indication of a value atrisk and/or cash flow obligation rather than a performance bond. Thedisclosed embodiments may also be used by market participants or otherentities to forecast or predict the effects of a prospective position onthe margin requirement of the market participant.

Trading Environment

The embodiments may be described in terms of a distributed computingsystem. The particular examples identify a specific set of componentsuseful in a futures and options exchange. However, many of thecomponents and inventive features are readily adapted to otherelectronic trading environments. The specific examples described hereinmay teach specific protocols and/or interfaces, although it should beunderstood that the principals involved may be extended to, or appliedin, other protocols and interfaces.

It should be appreciated that the plurality of entities utilizing orinvolved with the disclosed embodiments, e.g., the market participants,may be referred to by other nomenclature reflecting the role that theparticular entity is performing with respect to the disclosedembodiments and that a given entity may perform more than one roledepending upon the implementation and the nature of the particulartransaction being undertaken, as well as the entity's contractual and/orlegal relationship with another market participant and/or the exchange.

An exemplary trading network environment for implementing tradingsystems and methods is shown in FIG. 1. An exchange computer system 100receives messages that include orders and transmits market data relatedto orders and trades to users, such as via wide area network 162 and/orlocal area network 160 and computer devices 150, 152, 154, 156 and 158,as described herein, coupled with the exchange computer system 100.

Herein, the phrase “coupled with” is defined to mean directly connectedto or indirectly connected through one or more intermediate components.Such intermediate components may include both hardware and softwarebased components. Further, to clarify the use in the pending claims andto hereby provide notice to the public, the phrases “at least one of<A>, <B>, . . . and <N>” or “at least one of <A>, <B>, <N>, orcombinations thereof” are defined by the Applicant in the broadestsense, superseding any other implied definitions herebefore orhereinafter unless expressly asserted by the Applicant to the contrary,to mean one or more elements selected from the group comprising A, B, .. . and N, that is to say, any combination of one or more of theelements A, B, . . . or N including any one element alone or incombination with one or more of the other elements which may alsoinclude, in combination, additional elements not listed.

The exchange computer system 100 may be implemented with one or moremainframe, desktop or other computers, such as the example computer 200described herein with respect to FIG. 2.

A user database 102 may be provided which includes informationidentifying traders and other users of exchange computer system 100,such as account numbers or identifiers, user names and passwords. Anaccount data module 104 may be provided which may process accountinformation that may be used during trades.

A match engine module 106 may be included to match bid and offer pricesand may be implemented with software that executes one or morealgorithms for matching bids and offers. A trade database 108 may beincluded to store information identifying trades and descriptions oftrades. In particular, a trade database may store informationidentifying the time that a trade took place and the contract price. Anorder book module 110 may be included to compute or otherwise determinecurrent bid and offer prices, e.g., in a continuous auction market, oralso operate as an order accumulation buffer for a batch auction market.

A market data module 112 may be included to collect market data andprepare the data for transmission to users.

A risk management module 114 may be included to compute and determine auser's risk utilization in relation to the user's defined riskthresholds. The risk management module 114 may also be configured todetermine risk assessments or exposure levels in connection withpositions held by a market participant. The risk management module 114may be configured to administer, manage or maintain one or moremargining mechanisms implemented by the exchange computer system 100.Such administration, management or maintenance may include managing anumber of database records reflective of margin accounts of the marketparticipants. In some embodiments, the risk management module 114implements one or more aspects of the disclosed embodiments, including,for instance, principal component analysis (PCA) based margining, inconnection with interest rate swap (IRS) portfolios, as describedherein.

A message management module 116 may be included to, among other things,receive, and extract orders from, electronic data transaction requestmessages. The message management module 116 may define a point ofingress into the exchange computer system 100 where messages are orderedand considered to be received by the system. This may be considered apoint of determinism in the exchange computer system 100 that definesthe earliest point where the system can ascribe an order of receipt toarriving messages. The point of determinism may or may not be at or nearthe demarcation point between the exchange computer system 100 and apublic/internet network infrastructure. The message management module116 processes messages by interpreting the contents of a message basedon the message transmit protocol, such as the transmission controlprotocol (“TCP”), to provide the content of the message for furtherprocessing by the exchange computer system.

The message management module 116 may also be configured to detectcharacteristics of an order for a transaction to be undertaken in anelectronic marketplace. For example, the message management module 116may identify and extract order content such as a price, product, volume,and associated market participant for an order. The message managementmodule 116 may also identify and extract data indicating an action to beexecuted by the exchange computer system 100 with respect to theextracted order. For example, the message management module 116 maydetermine the transaction type of the transaction requested in a givenmessage. A message may include an instruction to perform a type oftransaction. The transaction type may be, in one embodiment, arequest/offer/order to either buy or sell a specified quantity or unitsof a financial instrument at a specified price or value. The messagemanagement module 116 may also identify and extract other orderinformation and other actions associated with the extracted order. Allextracted order characteristics, other information, and associatedactions extracted from a message for an order may be collectivelyconsidered an order as described and referenced herein.

Order or message characteristics may include, for example, the state ofthe system after a message is received, arrival time (e.g., the time amessage arrives at the MSG or Market Segment Gateway), message type(e.g., new, modify, cancel), and the number of matches generated by amessage. Order or message characteristics may also include marketparticipant side (e.g., buyer or seller) or time in force (e.g., a gooduntil end of day order that is good for the full trading day, a gooduntil canceled ordered that rests on the order book until matched, or afill or kill order that is canceled if not filled immediately, or a filland kill order (FOK) that is filled to the maximum amount possible, andany remaining or unfilled/unsatisfied quantity is not stored on thebooks or allowed to rest).

An order processing module 118 may be included to decompose delta-based,spread instrument, bulk and other types of composite orders forprocessing by the order book module 110 and/or the match engine module106. The order processing module 118 may also be used to implement oneor more procedures related to clearing an order. The order may becommunicated from the message management module 118 to the orderprocessing module 118. The order processing module 118 may be configuredto interpret the communicated order, and manage the ordercharacteristics, other information, and associated actions as they areprocessed through an order book module 110 and eventually transacted onan electronic market. For example, the order processing module 118 maystore the order characteristics and other content and execute theassociated actions. In an embodiment, the order processing module mayexecute an associated action of placing the order into an order book foran electronic trading system managed by the order book module 110. In anembodiment, placing an order into an order book and/or into anelectronic trading system may be considered a primary action for anorder. The order processing module 118 may be configured in variousarrangements, and may be configured as part of the order book module110, part of the message management module 118, or as an independentfunctioning module.

As an intermediary to electronic trading transactions, the exchangebears a certain amount of risk in each transaction that takes place. Tothat end, the clearing house implements risk management mechanisms toprotect the exchange. One or more of the modules of the exchangecomputer system 100 may be configured to determine settlement prices forconstituent contracts, such as deferred month contracts, of spreadinstruments, such as for example, settlement module 120. A settlementmodule 120 (or settlement processor or other payment processor) may beincluded to provide one or more functions related to settling orotherwise administering transactions cleared by the exchange. Settlementmodule 120 of the exchange computer system 100 may implement one or moresettlement price determination techniques. Settlement-related functionsneed not be limited to actions or events occurring at the end of acontract term. For instance, in some embodiments, settlement-relatedfunctions may include or involve daily or other mark to marketsettlements for margining purposes. In some cases, the settlement module120 may be configured to communicate with the trade database 108 (or thememory(ies) on which the trade database 108 is stored) and/or todetermine a payment amount based on a spot price, the price of thefutures contract or other financial instrument, or other price data, atvarious times. The determination may be made at one or more points intime during the term of the financial instrument in connection with amargining mechanism. For example, the settlement module 120 may be usedto determine a mark to market amount on a daily basis during the term ofthe financial instrument. Such determinations may also be made on asettlement date for the financial instrument for the purposes of finalsettlement.

In some embodiments, the settlement module 120 may be integrated to anydesired extent with one or more of the other modules or processors ofthe exchange computer system 100. For example, the settlement module 120and the risk management module 114 may be integrated to any desiredextent. In some cases, one or more margining procedures or other aspectsof the margining mechanism(s) may be implemented by the settlementmodule 120.

A forward rate module 122 may be coupled with the match engine module106 and/or order book module 110, or any other module necessary toprovide the requisite data as described below. As will be discussed inmore detail below, the forward rate module 122 monitors the trading ofparticular futures contracts and, based thereon, generates forwardinterest rates for select time periods. These forward interest rates maybe output one or more market participants and/or communicated to othermodules of the exchange computer system 100 to be utilized in thecalculation of other data, such as values of positions in interest rateinstrument products held by market participants, e.g. stored in the userdatabase, such as for computation of risk of loss by the risk managementmodule 114.

One or more of the above-described modules of the exchange computersystem 100 may be used to gather or obtain data to support thesettlement price determination, as well as a subsequent marginrequirement determination. For example, the order book module 110 and/orthe market data module 112 may be used to receive, access, or otherwiseobtain market data, such as bid-offer values of orders currently on theorder books. The trade database 108 may be used to receive, access, orotherwise obtain trade data indicative of the prices and volumes oftrades that were recently executed in a number of markets. In somecases, transaction data (and/or bid/ask data) may be gathered orobtained from open outcry pits and/or other sources and incorporatedinto the trade and market data from the electronic trading system(s).

It should be appreciated that concurrent processing limits may bedefined by or imposed separately or in combination on one or more of thetrading system components, including the user database 102, the accountdata module 104, the match engine module 106, the trade database 108,the order book module 110, the market data module 112, the riskmanagement module 114, the message management module 116, the orderprocessing module 118, the settlement module 120, the forward ratemodule 122, or other component of the exchange computer system 100.

The disclosed mechanisms may be implemented at any logical and/orphysical point(s), or combinations thereof, at which the relevantinformation/data (e.g., message traffic and responses thereto) may bemonitored or flows or is otherwise accessible or measurable, includingone or more gateway devices, modems, the computers or terminals of oneor more market participants, e.g., client computers, etc.

One skilled in the art will appreciate that one or more modulesdescribed herein may be implemented using, among other things, atangible computer-readable medium comprising computer-executableinstructions (e.g., executable software code). Alternatively, modulesmay be implemented as software code, firmware code, specificallyconfigured hardware or processors, and/or a combination of theaforementioned. For example, the modules may be embodied as part of anexchange 100 for financial instruments. It should be appreciated thedisclosed embodiments may be implemented as a different or separatemodule of the exchange computer system 100, or a separate computersystem coupled with the exchange computer system 100 so as to haveaccess to margin account record, pricing, and/or other data. Asdescribed herein, the disclosed embodiments may be implemented as acentrally accessible system or as a distributed system, e.g., where someof the disclosed functions are performed by the computer systems of themarket participants.

The trading network environment shown in FIG. 1 includes exemplarycomputer devices 150, 152, 154, 156 and 158 which depict differentexemplary methods or media by which a computer device may be coupledwith the exchange computer system 100 or by which a user maycommunicate, e.g., send and receive, trade or other informationtherewith. It should be appreciated that the types of computer devicesdeployed by traders and the methods and media by which they communicatewith the exchange computer system 100 is implementation dependent andmay vary and that not all of the depicted computer devices and/ormeans/media of communication may be used and that other computer devicesand/or means/media of communications, now available or later developedmay be used. Each computer device, which may comprise a computer 200described in more detail with respect to FIG. 2, may include a centralprocessor, specifically configured or otherwise, that controls theoverall operation of the computer and a system bus that connects thecentral processor to one or more conventional components, such as anetwork card or modem. Each computer device may also include a varietyof interface units and drives for reading and writing data or files andcommunicating with other computer devices and with the exchange computersystem 100. Depending on the type of computer device, a user caninteract with the computer with a keyboard, pointing device, microphone,pen device or other input device now available or later developed.

An exemplary computer device 150 is shown directly connected to exchangecomputer system 100, such as via a T1 line, a common local area network(LAN) or other wired and/or wireless medium for connecting computerdevices, such as the network 220 shown in FIG. 2 and described withrespect thereto. The exemplary computer device 150 is further shownconnected to a radio 168. The user of radio 168, which may include acellular telephone, smart phone, or other wireless proprietary and/ornon-proprietary device, may be a trader or exchange employee. The radiouser may transmit orders or other information to the exemplary computerdevice 150 or a user thereof. The user of the exemplary computer device150, or the exemplary computer device 150 alone and/or autonomously, maythen transmit the trade or other information to the exchange computersystem 100.

Exemplary computer devices 152 and 154 are coupled with a local areanetwork (“LAN”) 160 which may be configured in one or more of thewell-known LAN topologies, e.g., star, daisy chain, etc., and may use avariety of different protocols, such as Ethernet, TCP/IP, etc. Theexemplary computer devices 152 and 154 may communicate with each otherand with other computer and other devices which are coupled with the LAN160. Computer and other devices may be coupled with the LAN 160 viatwisted pair wires, coaxial cable, fiber optics or other wired orwireless media. As shown in FIG. 1, an exemplary wireless personaldigital assistant device (“PDA”) 158, such as a mobile telephone, tabletbased compute device, or other wireless device, may communicate with theLAN 160 and/or the Internet 162 via radio waves, such as via WiFi,Bluetooth and/or a cellular telephone based data communicationsprotocol. PDA 158 may also communicate with exchange computer system 100via a conventional wireless hub 164.

FIG. 1 also shows the LAN 160 coupled with a wide area network (“WAN”)162 which may be comprised of one or more public or private wired orwireless networks. In one embodiment, the WAN 162 includes the Internet162. The LAN 160 may include a router to connect LAN 160 to the Internet162. Exemplary computer device 156 is shown coupled directly to theInternet 162, such as via a modem, DSL line, satellite dish or any otherdevice for connecting a computer device to the Internet 162 via aservice provider therefore as is known. LAN 160 and/or WAN 162 may bethe same as the network 220 shown in FIG. 2 and described with respectthereto.

Users of the exchange computer system 100 may include one or more marketmakers 166 which may maintain a market by providing constant bid andoffer prices for a derivative or security to the exchange computersystem 100, such as via one of the exemplary computer devices depicted.The exchange computer system 100 may also exchange information withother match or trade engines, such as trade engine 170. One skilled inthe art will appreciate that numerous additional computers and systemsmay be coupled to exchange computer system 100. Such computers andsystems may include clearing, regulatory and fee systems.

The operations of computer devices and systems shown in FIG. 1 may becontrolled by computer-executable instructions stored on anon-transitory computer-readable medium. For example, the exemplarycomputer device 152 may store computer-executable instructions forreceiving order information from a user, transmitting that orderinformation to exchange computer system 100 in electronic messages,extracting the order information from the electronic messages, executingactions relating to the messages, and/or calculating values fromcharacteristics of the extracted order to facilitate matching orders andexecuting trades. In another example, the exemplary computer device 154may include computer-executable instructions for receiving market datafrom exchange computer system 100 and displaying that information to auser.

Numerous additional servers, computers, handheld devices, personaldigital assistants, telephones and other devices may also be connectedto exchange computer system 100. Moreover, one skilled in the art willappreciate that the topology shown in FIG. 1 is merely an example andthat the components shown in FIG. 1 may include other components notshown and be connected by numerous alternative topologies.

Referring now to FIG. 2, an illustrative embodiment of a generalcomputer system 200 is shown. The computer system 200 can include a setof instructions that can be executed to cause the computer system 200 toperform any one or more of the methods or computer based functionsdisclosed herein. The computer system 200 may operate as a standalonedevice or may be connected, e.g., using a network, to other computersystems or peripheral devices. Any of the components discussed herein,such as processor 202, may be a computer system 200 or a component inthe computer system 200. The computer system 200 may be specificallyconfigured to implement a match engine, margin processing, payment orclearing function on behalf of an exchange, such as the ChicagoMercantile Exchange, of which the disclosed embodiments are a componentthereof.

In a networked deployment, the computer system 200 may operate in thecapacity of a server or as a client user computer in a client-serveruser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 200 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a mobile device, a palmtop computer, a laptopcomputer, a desktop computer, a communications device, a wirelesstelephone, a land-line telephone, a control system, a camera, a scanner,a facsimile machine, a printer, a pager, a personal trusted device, aweb appliance, a network router, switch or bridge, or any other machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine. In a particularembodiment, the computer system 200 can be implemented using electronicdevices that provide voice, video or data communication. Further, whilea single computer system 200 is illustrated, the term “system” shallalso be taken to include any collection of systems or sub-systems thatindividually or jointly execute a set, or multiple sets, of instructionsto perform one or more computer functions.

As illustrated in FIG. 2, the computer system 200 may include aprocessor 202, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 202 may be a component ina variety of systems. For example, the processor 202 may be part of astandard personal computer or a workstation. The processor 202 may beone or more general processors, digital signal processors, specificallyconfigured processors, application specific integrated circuits, fieldprogrammable gate arrays, servers, networks, digital circuits, analogcircuits, combinations thereof, or other now known or later developeddevices for analyzing and processing data. The processor 202 mayimplement a software program, such as code generated manually (i.e.,programmed).

The computer system 200 may include a memory 204 that can communicatevia a bus 208. The memory 204 may be a main memory, a static memory, ora dynamic memory. The memory 204 may include, but is not limited to,computer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In oneembodiment, the memory 204 includes a cache or random access memory forthe processor 202. In alternative embodiments, the memory 204 isseparate from the processor 202, such as a cache memory of a processor,the system memory, or other memory. The memory 204 may be an externalstorage device or database for storing data. Examples include a harddrive, compact disc (“CD”), digital video disc (“DVD”), memory card,memory stick, floppy disc, universal serial bus (“USB”) memory device,or any other device operative to store data. The memory 204 is operableto store instructions executable by the processor 202. The functions,acts or tasks illustrated in the figures or described herein may beperformed by the programmed processor 202 executing the instructions 212stored in the memory 204. The functions, acts or tasks are independentof the particular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 200 may further include a display unit214, such as a liquid crystal display (LCD), an organic light emittingdiode (OLED), a flat panel display, a solid state display, a cathode raytube (CRT), a projector, a printer or other now known or later developeddisplay device for outputting determined information. The display 214may act as an interface for the user to see the functioning of theprocessor 202, or specifically as an interface with the software storedin the memory 204 or in the drive unit 206.

Additionally, the computer system 200 may include an input device 216configured to allow a user to interact with any of the components ofsystem 200. The input device 216 may be a number pad, a keyboard, or acursor control device, such as a mouse, or a joystick, touch screendisplay, remote control or any other device operative to interact withthe system 200.

In a particular embodiment, as depicted in FIG. 2, the computer system200 may also include a disk or optical drive unit 206. The disk driveunit 206 may include a computer-readable medium 210 in which one or moresets of instructions 212, e.g., software, can be embedded. Further, theinstructions 212 may embody one or more of the methods or logic asdescribed herein. In a particular embodiment, the instructions 212 mayreside completely, or at least partially, within the memory 204 and/orwithin the processor 202 during execution by the computer system 200.The memory 204 and the processor 202 also may include computer-readablemedia as discussed herein.

The present disclosure contemplates a computer-readable medium thatincludes instructions 212 or receives and executes instructions 212responsive to a propagated signal, so that a device connected to anetwork 220 can communicate voice, video, audio, images or any otherdata over the network 220. Further, the instructions 212 may betransmitted or received over the network 220 via a communicationinterface 218. The communication interface 218 may be a part of theprocessor 202 or may be a separate component. The communicationinterface 218 may be created in software or may be a physical connectionin hardware. The communication interface 218 is configured to connectwith a network 220, external media, the display 214, or any othercomponents in system 200, or combinations thereof. The connection withthe network 220 may be a physical connection, such as a wired Ethernetconnection or may be established wirelessly. Likewise, the additionalconnections with other components of the system 200 may be physicalconnections or may be established wirelessly.

The network 220 may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork 220 may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to, TCP/IP based networking protocols.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe subject matter described in this specification can be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, or a combination of one or more ofthem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated or otherwise specificallyconfigured hardware implementations, such as application specificintegrated circuits, programmable logic arrays and other hardwaredevices, can be constructed to implement one or more of the methodsdescribed herein. Applications that may include the apparatus andsystems of various embodiments can broadly include a variety ofelectronic and computer systems. One or more embodiments describedherein may implement functions using two or more specific interconnectedhardware modules or devices with related control and data signals thatcan be communicated between and through the modules, or as portions ofan application-specific integrated circuit. Accordingly, the presentsystem encompasses software, firmware, and hardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

As used herein, the terms “microprocessor” or “general-purposeprocessor” (“GPP”) may refer to a hardware device that fetchesinstructions and data from a memory or storage device and executes thoseinstructions (for example, an Intel Xeon processor or an AMD Opteronprocessor) to then, for example, process the data in accordancetherewith. The term “reconfigurable logic” may refer to any logictechnology whose form and function can be significantly altered (i.e.,reconfigured) in the field post-manufacture as opposed to amicroprocessor, whose function can change post-manufacture, e.g. viacomputer executable software code, but whose form, e.g. thearrangement/layout and interconnection of logical structures, is fixedat manufacture. The term “software” may refer to data processingfunctionality that is deployed on a GPP. The term “firmware” may referto data processing functionality that is deployed on reconfigurablelogic. One example of a reconfigurable logic is a field programmablegate array (“FPGA”) which is a reconfigurable integrated circuit. AnFPGA may contain programmable logic components called “logic blocks”,and a hierarchy of reconfigurable interconnects that allow the blocks tobe “wired together”, somewhat like many (changeable) logic gates thatcan be inter-wired in (many) different configurations. Logic blocks maybe configured to perform complex combinatorial functions, or merelysimple logic gates like AND, OR, NOT and XOR. An FPGA may furtherinclude memory elements, which may be simple flip-flops or more completeblocks of memory.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. Feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback. Input from the user can bereceived in any form, including acoustic, speech, or tactile input.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., a data server, or that includes a middleware component, e.g., anapplication server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

It should be appreciated that the disclosed embodiments may beapplicable to other types of messages depending upon the implementation.Further, the messages may comprise one or more data packets, datagramsor other collection of data formatted, arranged configured and/orpackaged in a particular one or more protocols, e.g., the FIX protocol,TCP/IP, Ethernet, etc., suitable for transmission via a network 214 aswas described, such as the message format and/or protocols described inU.S. Pat. No. 7,831,491 and U.S. Patent Publication No. 2005/0096999 A1,both of which are incorporated by reference herein in their entiretiesand relied upon. Further, the disclosed message management system may beimplemented using an open message standard implementation, such as FIX,FIX Binary, FIX/FAST, or by an exchange-provided API.

The embodiments described herein utilize trade related electronicmessages such as mass quote messages, individual order messages,modification messages, cancellation messages, etc., so as to enacttrading activity in an electronic market. The trading entity and/ormarket participant may have one or multiple trading terminals associatedwith the session. Furthermore, the financial instruments may befinancial derivative products. Derivative products may include futurescontracts, options on futures contracts, futures contracts that arefunctions of or related to other futures contracts, swaps, swaptions, orother financial instruments that have their price related to or derivedfrom an underlying product, security, commodity, equity, index, orinterest rate product. In one embodiment, the orders are for optionscontracts that belong to a common option class. Orders may also be forbaskets, quadrants, other combinations of financial instruments, etc.The option contracts may have a plurality of strike prices and/orcomprise put and call contracts. A mass quote message may be received atan exchange. As used herein, an exchange computing system 100 includes aplace or system that receives and/or executes orders.

In an embodiment, a plurality of electronic messages is received fromthe network. The plurality of electronic messages may be received at anetwork interface for the electronic trading system. The plurality ofelectronic messages may be sent from market participants. The pluralityof messages may include order characteristics and be associated withactions to be executed with respect to an order that may be extractedfrom the order characteristics. The action may involve any action asassociated with transacting the order in an electronic trading system.The actions may involve placing the orders within a particular marketand/or order book of a market in the electronic trading system.

In an embodiment, an incoming transaction may be received. The incomingtransaction may be from, and therefore associated with, a marketparticipant of an electronic market managed by an electronic tradingsystem. The transaction may involve an order as extracted from areceived message, and may have an associated action. The actions mayinvolve placing an order to buy or sell a financial product in theelectronic market, or modifying or deleting such an order. In anembodiment, the financial product may be based on an associatedfinancial instrument which the electronic market is established totrade.

In an embodiment, the action associated with the transaction isdetermined. For example, it may be determined whether the incomingtransaction comprises an order to buy or sell a quantity of theassociated financial instrument or an order to modify or cancel anexisting order in the electronic market. Orders to buy or sell andorders to modify or cancel may be acted upon differently by theelectronic market. For example, data indicative of differentcharacteristics of the types of orders may be stored.

In an embodiment, data relating to the received transaction is stored.The data may be stored in any device, or using any technique, operableto store and provide recovery of data. For example, a memory 204 orcomputer readable medium 210, may be used to store data, as is describedwith respect to FIG. 2 in further detail herein. Data may be storedrelating received transactions for a period of time, indefinitely, orfor a rolling most recent time period such that the stored data isindicative of the market participant's recent activity in the electronicmarket.

If and/or when a transaction is determined to be an order to modify orcancel a previously placed, or existing, order, data indicative of theseactions may be stored. For example, data indicative of a running countof a number or frequency of the receipt of modify or cancel orders fromthe market participant may be stored. A number may be a total number ofmodify or cancel orders received from the market participant, or anumber of modify or cancel orders received from the market participantover a specified time. A frequency may be a time based frequency, as ina number of cancel or modify orders per unit of time, or a number ofcancel or modify orders received from the market participant as apercentage of total transactions received from the participant, whichmay or may not be limited by a specified length of time.

If and/or when a transaction is determined to be an order to buy or sella financial product, or financial instrument, other indicative data maybe stored. For example, data indicative of quantity and associated priceof the order to buy or sell may be stored.

Data indicative of attempts to match incoming orders may also be stored.The data may be stored in any device, or using any technique, operableto store and provide recovery of data. For example, a memory 204 orcomputer readable medium 210, may be used to store data, as is describedwith respect to FIG. 2. The acts of the process as described herein mayalso be repeated. As such, data for multiple received transactions formultiple market participants may be stored and used as describe herein.

The order processing module 118 may also store data indicative ofcharacteristics of the extracted orders. For example, the orderprocessing module may store data indicative of orders having anassociated modify or cancel action, such as by recording a count of thenumber of such orders associated with particular market participants.The order processing module may also store data indicative of quantitiesand associated prices of orders to buy or sell a product placed in themarket order book 110, as associated with particular marketparticipants.

Also, the order processing module 118 may be configured to calculate andassociate with particular orders a value indicative of an associatedmarket participant's market activity quality, which is a valueindicative of whether the market participant's market activity increasesor tends to increase liquidity of a market. This value may be determinedbased on the price of the particular order, previously stored quantitiesof orders from the associated market participant, the previously storeddata indicative of previously received orders to modify or cancel asassociated with the market participant, and previously stored dataindicative of a result of the attempt to match previously receivedorders stored in association with the market participant. The orderprocessing module 118 may determine or otherwise calculate scoresindicative of the quality value based on these stored extracted ordercharacteristics, such as an MQI as described herein.

Further, electronic trading systems may perform actions on orders placedfrom received messages based on various characteristics of the messagesand/or market participants associated with the messages. These actionsmay include matching the orders either during a continuous auctionprocess, or at the conclusion of a collection period during a batchauction process. The matching of orders may be by any technique.

The matching of orders may occur based on a priority indicated by thecharacteristics of orders and market participants associated with theorders. Orders having a higher priority may be matched before orders ofa lower priority. Such priority may be determined using varioustechniques. For example, orders that were indicated by messages receivedearlier may receive a higher priority to match than orders that wereindicated by messages received later. Also, scoring or grading of thecharacteristics may provide for priority determination. Data indicativeof order matches may be stored by a match engine and/or an orderprocessing module 118, and used for determining MQI scores of marketparticipants.

Example Users

Generally, a market may involve market makers, such as marketparticipants who consistently provide bids and/or offers at specificprices in a manner typically conducive to balancing risk, and markettakers who may be willing to execute transactions at prevailing bids oroffers may be characterized by more aggressive actions so as to maintainrisk and/or exposure as a speculative investment strategy. From analternate perspective, a market maker may be considered a marketparticipant who places an order to sell at a price at which there is nopreviously or concurrently provided counter order. Similarly, a markettaker may be considered a market participant who places an order to buyat a price at which there is a previously or concurrently providedcounter order. A balanced and efficient market may involve both marketmakers and market takers, coexisting in a mutually beneficial basis. Themutual existence, when functioning properly, may facilitate liquidity inthe market such that a market may exist with “tight” bid-ask spreads(e.g., small difference between bid and ask prices) and a “deep” volumefrom many currently provided orders such that large quantity orders maybe executed without driving prices significantly higher or lower.

As such, both market participant types are useful in generatingliquidity in a market, but specific characteristics of market activitytaken by market participants may provide an indication of a particularmarket participant's effect on market liquidity. For example, a MarketQuality Index (“MQI”) of an order may be determined using thecharacteristics. An MQI may be considered a value indicating alikelihood that a particular order will improve or facilitate liquidityin a market. That is, the value may indicate a likelihood that the orderwill increase a probability that subsequent requests and transactionfrom other market participants will be satisfied. As such, an MQI may bedetermined based on a proximity of the entered price of an order to amidpoint of a current bid-ask price spread, a size of the entered order,a volume or quantity of previously filled orders of the marketparticipant associated with the order, and/or a frequency ofmodifications to previous orders of the market participant associatedwith the order. In this way, an electronic trading system may functionto assess and/or assign an MQI to received electronic messages toestablish messages that have a higher value to the system, and thus thesystem may use computing resources more efficiently by expendingresources to match orders of the higher value messages prior toexpending resources of lower value messages.

While an MQI may be applied to any or all market participants, such anindex may also be applied only to a subset thereof, such as large marketparticipants, or market participants whose market activity as measuredin terms of average daily message traffic over a limited historical timeperiod exceeds a specified number. For example, a market participantgenerating more than 500, 1,000, or even 10,000 market messages per daymay be considered a large market participant.

An exchange provides one or more markets for the purchase and sale ofvarious types of products including financial instruments such asstocks, bonds, futures contracts, options, currency, cash, and othersimilar instruments. Agricultural products and commodities are alsoexamples of products traded on such exchanges. A futures contract is aproduct that is a contract for the future delivery of another financialinstrument such as a quantity of grains, metals, oils, bonds, currency,or cash. Generally, each exchange establishes a specification for eachmarket provided thereby that defines at least the product traded in themarket, minimum quantities that must be traded, and minimum changes inprice (e.g., tick size). For some types of products (e.g., futures oroptions), the specification further defines a quantity of the underlyingproduct represented by one unit (or lot) of the product, and deliveryand expiration dates. As will be described, the exchange may furtherdefine the matching algorithm, or rules, by which incoming orders willbe matched/allocated to resting orders.

Matching and Transaction Processing

Market participants, e.g., traders, use software to send orders ormessages to the trading platform. The order identifies the product, thequantity of the product the trader wishes to trade, a price at which thetrader wishes to trade the product, and a direction of the order (i.e.,whether the order is a bid, i.e., an offer to buy, or an ask, i.e., anoffer to sell). It will be appreciated that there may be other ordertypes or messages that traders can send including requests to modify orcancel a previously submitted order.

The exchange computer system monitors incoming orders received therebyand attempts to identify, i.e., match or allocate, as described herein,one or more previously received, but not yet matched, orders, i.e.,limit orders to buy or sell a given quantity at a given price, referredto as “resting” orders, stored in an order book database, wherein eachidentified order is contra to the incoming order and has a favorableprice relative to the incoming order. An incoming order may be an“aggressor” order, i.e., a market order to sell a given quantity atwhatever may be the current resting bid order price(s) or a market orderto buy a given quantity at whatever may be the current resting ask orderprice(s). An incoming order may be a “market making” order, i.e., amarket order to buy or sell at a price for which there are currently noresting orders. In particular, if the incoming order is a bid, i.e., anoffer to buy, then the identified order(s) will be an ask, i.e., anoffer to sell, at a price that is identical to or higher than the bidprice. Similarly, if the incoming order is an ask, i.e., an offer tosell, the identified order(s) will be a bid, i.e., an offer to buy, at aprice that is identical to or lower than the offer price.

An exchange computing system may receive conditional orders or messagesfor a data object, where the order may include two prices or values: areference value and a stop value. A conditional order may be configuredso that when a product represented by the data object trades at thereference price, the stop order is activated at the stop value. Forexample, if the exchange computing system's order management moduleincludes a stop order with a stop price of 5 and a limit price of 1 fora product, and a trade at 5 (i.e., the stop price of the stop order)occurs, then the exchange computing system attempts to trade at 1 (i.e.,the limit price of the stop order). In other words, a stop order is aconditional order to trade (or execute) at the limit price that istriggered (or elected) when a trade at the stop price occurs.

Stop orders also rest on, or are maintained in, an order book to monitorfor a trade at the stop price, which triggers an attempted trade at thelimit price. In some embodiments, a triggered limit price for a stoporder may be treated as an incoming order.

Upon identification (matching) of a contra order(s), a minimum of thequantities associated with the identified order and the incoming orderis matched and that quantity of each of the identified and incomingorders become two halves of a matched trade that is sent to a clearinghouse. The exchange computer system considers each identified order inthis manner until either all of the identified orders have beenconsidered or all of the quantity associated with the incoming order hasbeen matched, i.e., the order has been filled. If any quantity of theincoming order remains, an entry may be created in the order bookdatabase and information regarding the incoming order is recordedtherein, i.e., a resting order is placed on the order book for theremaining quantity to await a subsequent incoming order counter thereto.

It should be appreciated that in electronic trading systems implementedvia an exchange computing system, a trade price (or match value) maydiffer from (i.e., be better for the submitter, e.g., lower than asubmitted buy price or higher than a submitted sell price) the limitprice that is submitted, e.g., a price included in an incoming message,or a triggered limit price from a stop order.

As used herein, “better” than a reference value means lower than thereference value if the transaction is a purchase (or acquire)transaction, and higher than the reference value if the transaction is asell transaction. Said another way, for purchase (or acquire)transactions, lower values are better, and for relinquish or selltransactions, higher values are better.

Traders access the markets on a trading platform using trading softwarethat receives and displays at least a portion of the order book for amarket, i.e., at least a portion of the currently resting orders,enables a trader to provide parameters for an order for the producttraded in the market, and transmits the order to the exchange computersystem. The trading software typically includes a graphical userinterface to display at least a price and quantity of some of theentries in the order book associated with the market. The number ofentries of the order book displayed is generally preconfigured by thetrading software, limited by the exchange computer system, or customizedby the user. Some graphical user interfaces display order books ofmultiple markets of one or more trading platforms. The trader may be anindividual who trades on his/her behalf, a broker trading on behalf ofanother person or entity, a group, or an entity. Furthermore, the tradermay be a system that automatically generates and submits orders.

If the exchange computer system identifies that an incoming market ordermay be filled by a combination of multiple resting orders, e.g., theresting order at the best price only partially fills the incoming order,the exchange computer system may allocate the remaining quantity of theincoming, i.e., that which was not filled by the resting order at thebest price, among such identified orders in accordance withprioritization and allocation rules/algorithms, referred to as“allocation algorithms” or “matching algorithms,” as, for example, maybe defined in the specification of the particular financial product ordefined by the exchange for multiple financial products. Similarly, ifthe exchange computer system identifies multiple orders contra to theincoming limit order and that have an identical price which is favorableto the price of the incoming order, i.e., the price is equal to orbetter, e.g., lower if the incoming order is a buy (or instruction topurchase, or instruction to acquire) or higher if the incoming order isa sell (or instruction to relinquish), than the price of the incomingorder, the exchange computer system may allocate the quantity of theincoming order among such identified orders in accordance with thematching algorithms as, for example, may be defined in the specificationof the particular financial product or defined by the exchange formultiple financial products.

An exchange responds to inputs, such as trader orders, cancellation,etc., in a manner as expected by the market participants, such as basedon market data, e.g., prices, available counter-orders, etc., to providean expected level of certainty that transactions will occur in aconsistent and predictable manner and without unknown or unascertainablerisks. Accordingly, the method by which incoming orders are matched withresting orders must be defined so that market participants have anexpectation of what the result will be when they place an order or haveresting orders and an incoming order is received, even if the expectedresult is, in fact, at least partially unpredictable due to somecomponent of the process being random or arbitrary or due to marketparticipants having imperfect or less than all information, e.g.,unknown position of an order in an order book. Typically, the exchangedefines the matching/allocation algorithm that will be used for aparticular financial product, with or without input from the marketparticipants. Once defined for a particular product, thematching/allocation algorithm is typically not altered, except inlimited circumstance, such as to correct errors or improve operation, soas not to disrupt trader expectations. It will be appreciated thatdifferent products offered by a particular exchange may use differentmatching algorithms.

For example, a first-in/first-out (FIFO) matching algorithm, alsoreferred to as a “Price Time” algorithm, considers each identified ordersequentially in accordance with when the identified order was received.The quantity of the incoming order is matched to the quantity of theidentified order at the best price received earliest, then quantities ofthe next earliest best price orders, and so on until the quantity of theincoming order is exhausted. Some product specifications define the useof a pro-rata matching algorithm, wherein a quantity of an incomingorder is allocated to each of plurality of identified ordersproportionally. Some exchange computer systems provide a priority tocertain standing orders in particular markets. An example of such anorder is the first order that improves a price (i.e., improves themarket) for the product during a trading session. To be given priority,the trading platform may require that the quantity associated with theorder is at least a minimum quantity. Further, some exchange computersystems cap the quantity of an incoming order that is allocated to astanding order on the basis of a priority for certain markets. Inaddition, some exchange computer systems may give a preference to orderssubmitted by a trader who is designated as a market maker for theproduct. Other exchange computer systems may use other criteria todetermine whether orders submitted by a particular trader are given apreference. Typically, when the exchange computer system allocates aquantity of an incoming order to a plurality of identified orders at thesame price, the trading host allocates a quantity of the incoming orderto any orders that have been given priority. The exchange computersystem thereafter allocates any remaining quantity of the incoming orderto orders submitted by traders designated to have a preference, and thenallocates any still remaining quantity of the incoming order using theFIFO or pro-rata algorithms. Pro-rata algorithms used in some marketsmay require that an allocation provided to a particular order inaccordance with the pro-rata algorithm must meet at least a minimumallocation quantity. Any orders that do not meet or exceed the minimumallocation quantity are allocated to on a FIFO basis after the pro-rataallocation (if any quantity of the incoming order remains). Moreinformation regarding order allocation may be found in U.S. Pat. No.7,853,499, the entirety of which is incorporated by reference herein andrelied upon. Other examples of matching algorithms which may be definedfor allocation of orders of a particular financial product include:Price Explicit Time; Order Level Pro Rata; Order Level Priority ProRata; Preference Price Explicit Time; Preference Order Level Pro Rata;Preference Order Level Priority Pro Rata; Threshold Pro-Rata; PriorityThreshold Pro-Rata; Preference Threshold Pro-Rata; Priority PreferenceThreshold Pro-Rata; and Split Price-Time Pro-Rata, which are describedin U.S. patent application Ser. No. 13/534,499, filed on Jun. 27, 2012,entitled “Multiple Trade Matching Algorithms,” published as U.S. PatentApplication Publication No. 2014/0006243 A1, the entirety of which isincorporated by reference herein and relied upon.

With respect to incoming orders, some traders, such as automated and/oralgorithmic traders, attempt to respond to market events, such as tocapitalize upon a mispriced resting order or other market inefficiency,as quickly as possible. This may result in penalizing the trader whomakes an errant trade, or whose underlying trading motivations havechanged, and who cannot otherwise modify or cancel their order fasterthan other traders can submit trades there against. It may consideredthat an electronic trading system that rewards the trader who submitstheir order first creates an incentive to either invest substantialcapital in faster trading systems, participate in the marketsubstantially to capitalize on opportunities (aggressor side/lower risktrading) as opposed to creating new opportunities (market making/higherrisk trading), modify existing systems to streamline business logic atthe cost of trade quality, or reduce one's activities and exposure inthe market. The result may be a lesser quality market and/or reducedtransaction volume, and corresponding thereto, reduced fees to theexchange.

With respect to resting orders, allocation/matching suitable restingorders to match against an incoming order can be performed, as describedherein, in many different ways. Generally, it will be appreciated thatallocation/matching algorithms are only needed when the incoming orderquantity is less than the total quantity of the suitable resting ordersas, only in this situation, is it necessary to decide which restingorder(s) will not be fully satisfied, which trader(s) will not get theirorders filled. It can be seen from the above descriptions of thematching/allocation algorithms, that they fall generally into threecategories: time priority/first-in-first-out (“FIFO”), pro rata, or ahybrid of FIFO and pro rata.

FIFO generally rewards the first trader to place an order at aparticular price and maintains this reward indefinitely. So if a traderis the first to place an order at price X, no matter how long that orderrests and no matter how many orders may follow at the same price, assoon as a suitable incoming order is received, that first trader will bematched first. This “first mover” system may commit other traders topositions in the queue after the first move traders. Furthermore, whileit may be beneficial to give priority to a trader who is first to placean order at a given price because that trader is, in effect, taking arisk, the longer that the trader's order rests, the less beneficial itmay be. For instance, it could deter other traders from adding liquidityto the marketplace at that price because they know the first mover (andpotentially others) already occupies the front of the queue.

With a pro rata allocation, incoming orders are effectively split amongsuitable resting orders. This provides a sense of fairness in thateveryone may get some of their order filled. However, a trader who tooka risk by being first to place an order (a “market turning” order) at aprice may end up having to share an incoming order with a much latersubmitted order. Furthermore, as a pro rata allocation distributes theincoming order according to a proportion based on the resting orderquantities, traders may place orders for large quantities, which theyare willing to trade but may not necessarily want to trade, in order toincrease the proportion of an incoming order that they will receive.This results in an escalation of quantities on the order book andexposes a trader to a risk that someone may trade against one of theseorders and subject the trader to a larger trade than they intended. Inthe typical case, once an incoming order is allocated against theselarge resting orders, the traders subsequently cancel the remainingresting quantity which may frustrate other traders. Accordingly, as FIFOand pro rata both have benefits and problems, exchanges may try to usehybrid allocation/matching algorithms which attempt to balance thesebenefits and problems by combining FIFO and pro rata in some manner.However, hybrid systems define conditions or fixed rules to determinewhen FIFO should be used and when pro rata should be used. For example,a fixed percentage of an incoming order may be allocated using a FIFOmechanism with the remainder being allocated pro rata.

Order Book Object Data Structures

In one embodiment, the messages and/or values received for each objectmay be stored in queues according to value and/or priority techniquesimplemented by an exchange computing system 100. FIG. 3 illustrates anexample data structure 300, which may be stored in a memory or otherstorage device, such as the memory 204 or storage device 206 describedwith respect to FIG. 2, for storing and retrieving messages related todifferent values for the same action for an object. For example, datastructure 300 may be a set of queues or linked lists for multiple valuesfor an action, e.g., bid, on an object. Data structure 300 may beimplemented as a database. It should be appreciated that the system maystore multiple values for the same action for an object, for example,because multiple users submitted messages to buy specified quantities ofan object at different values. Thus, in one embodiment, the exchangecomputing system may keep track of different orders or messages forbuying or selling quantities of objects at specified values.

Although the present patent application contemplates using queue datastructures for storing messages in a memory, the implementation mayinvolve additional pointers, i.e., memory address pointers, or linkingto other data structures. Incoming messages may be stored at anidentifiable memory address. The transaction processor can traversemessages in order by pointing to and retrieving different messages fromthe different memories. Thus, messages that may be depicted sequentiallymay actually be stored in memory in disparate locations. The softwareprograms implementing the transaction processing may retrieve andprocess messages in sequence from the various disparate (e.g., random)locations. Thus, in one embodiment, each queue may store differentvalues, which could represent prices, where each value points to or islinked to the messages (which may themselves be stored in queues andsequenced according to priority techniques, such as prioritizing byvalue) that will match at that value. For example, as shown in FIG. 3,all of the values relevant to executing an action at different valuesfor an object are stored in a queue. Each value in turn points to, e.g.,a linked list or queue logically associated with the values. The linkedlist stores the messages that instruct the exchange computing system tobuy specified quantities of the object at the corresponding value.

Transaction Processor Data Structures

FIGS. 4A and 4B illustrate an example embodiment of a data structureused to implement match engine module 106. Match engine module 106 mayinclude a conversion component 402, pre-match queue 404, match component406, post-match queue 408 and publish component 410.

Although the embodiments are disclosed as being implemented in queues,it should be understood that different data structures, such as forexample linked lists or trees, may also be used. Although theapplication contemplates using queue data structures for storingmessages in a memory, the implementation may involve additionalpointers, i.e., memory address pointers, or linking to other datastructures. Thus, in one embodiment, each incoming message may be storedat an identifiable memory address. The transaction processing componentscan traverse messages in order by pointing to and retrieving differentmessages from the different memories. Thus, messages that may beprocessed sequentially in queues may actually be stored in memory indisparate locations. The software programs implementing the transactionprocessing may retrieve and process messages in sequence from thevarious disparate (e.g., random) locations.

The queues described herein may, in one embodiment, be structured sothat the messages are stored in sequence according to time of receipt,e.g., they may be first-in/first-out (FIFO) queues.

The match engine module 106 may be an example of a transactionprocessing system. The pre-match queue 404 may be an example of apre-transaction queue. The match component 406 may be an example of atransaction component. The post-match queue 408 may be an example of apost-transaction queue. The publish component 410 may be an example of adistribution component. The transaction component may process messagesand generate transaction component results.

It should be appreciated that match engine module 106 may not includeall of the components described herein. For example, match engine module106 may only include pre-match queue 404 and match component 406, asshown in FIG. 4B. In one embodiment, the latency detection system maydetect how long a message waits in a pre-match queue 404 (e.g.,latency), and compares the latency to the maximum allowable latencyassociated with the message.

In one embodiment, the publish component may be a distribution componentthat can distribute data to one or more market participant computers. Inone embodiment, match engine module 106 operates according to afirst-in/first-out (FIFO) ordering. The conversion component 402converts or extracts a message received from a trader via the MarketSegment Gateway or MSG into a message format that can be input into thepre-match queue 404.

Messages from the pre-match queue may enter the match component 406sequentially and may be processed sequentially. In one regard, thepre-transaction queue, e.g., the pre-match queue, may be considered tobe a buffer or waiting spot for messages before they can enter and beprocessed by the transaction component, e.g., the match component. Thematch component matches orders, and the time a messages spends beingprocessed by the match component can vary, depending on the contents ofthe message and resting orders on the book. Thus, newly receivedmessages wait in the pre-transaction queue until the match component isready to process those messages. Moreover, messages are received andprocessed sequentially or in a first-in, first-out FIFO methodology. Thefirst message that enters the pre-match or pre-transaction queue will bethe first message to exit the pre-match queue and enter the matchcomponent. In one embodiment, there is no out-of-order messageprocessing for messages received by the transaction processing system.The pre-match and post-match queues are, in one embodiment, fixed insize, and any messages received when the queues are full may need towait outside the transaction processing system or be re-sent to thetransaction processing system.

The match component 406 processes an order or message, at which pointthe transaction processing system may consider the order or message ashaving been processed. The match component 406 may generate one messageor more than one message, depending on whether an incoming order wassuccessfully matched by the match component. An order message thatmatches against a resting order in the order book may generate dozens orhundreds of messages. For example, a large incoming order may matchagainst several smaller resting orders at the same price level. Forexample, if many orders match due to a new order message, the matchengine needs to send out multiple messages informing traders whichresting orders have matched. Or, an order message may not match anyresting order and only generate an acknowledgement message. Thus, thematch component 406 in one embodiment will generate at least onemessage, but may generate more messages, depending upon the activitiesoccurring in the match component. For example, the more orders that arematched due to a given message being processed by the match component,the more time may be needed to process that message. Other messagesbehind that given message will have to wait in the pre-match queue.

Messages resulting from matches in the match component 406 enter thepost-match queue 408. The post-match queue may be similar infunctionality and structure to the pre-match queue discussed above,e.g., the post-match queue is a FIFO queue of fixed size. As illustratedin FIGS. 4A and 4B, a difference between the pre- and post-match queuesmay be the location and contents of the structures, namely, thepre-match queue stores messages that are waiting to be processed,whereas the post-match queue stores match component results due tomatching by the match component. The match component receives messagesfrom the pre-match queue, and sends match component results to thepost-match queue. In one embodiment, the time that results messages,generated due to the transaction processing of a given message, spend inthe post-match queue is not included in the latency calculation for thegiven message.

Messages from the post-match queue 408 enter the publish component 410sequentially and are published via the MSG sequentially. Thus, themessages in the post-match queue 408 are an effect or result of themessages that were previously in the pre-match queue 404. In otherwords, messages that are in the pre-match queue 404 at any given timewill have an impact on or affect the contents of the post-match queue408, depending on the events that occur in the match component 406 oncethe messages in the pre-match queue 404 enter the match component 406.

As noted above, the match engine module 106 in one embodiment operatesin a first-in, first-out (FIFO) scheme. In other words, the firstmessage that enters the match engine module 106 is the first messagethat is processed by the match engine module 106. Thus, the match enginemodule 106 in one embodiment processes messages in the order themessages are received. In FIGS. 4A and 4B, as shown by the data flowarrow, data is processed sequentially by the illustrated structures fromleft to right, beginning at the conversion component 402, to thepre-match queue, to the match component 406, to the post-match queue408, and to the publish component 410. The overall transactionprocessing system operates in a FIFO scheme such that data flows fromelement 402 to 404 to 406 to 408 to 410, in that order. If any one ofthe queues or components of the transaction processing systemexperiences a delay, that creates a backlog for the structures precedingthe delayed structure. For example, if the match or transactioncomponent is undergoing a high processing volume, and if the pre-matchor pre-transaction queue is full of messages waiting to enter the matchor transaction component, the conversion component may not be able toadd any more messages to the pre-match or pre-transaction queue.

Messages wait in the pre-match queue. The time a message waits in thepre-match queue depends upon how many messages are ahead of that message(i.e., earlier messages), and how much time each of the earlier messagesspends being serviced or processed by the match component. Messages alsowait in the post-match queue. The time a message waits in the post-matchqueue depends upon how many messages are ahead of that message (i.e.,earlier messages), and how much time each of the earlier messages spendsbeing serviced or processed by the publish component. These wait timesmay be viewed as a latency that can affect a market participant'strading strategy.

After a message is published (after being processed by the componentsand/or queues of the match engine module), e.g., via a market data feed,the message becomes public information and is publically viewable andaccessible. Traders consuming such published messages may act upon thosemessage, e.g., submit additional new input messages to the exchangecomputing system responsive to the published messages.

The match component attempts to match aggressing or incoming ordersagainst resting orders. If an aggressing order does not match anyresting orders, then the aggressing order may become a resting order, oran order resting on the books. For example, if a message includes a neworder that is specified to have a one-year time in force, and the neworder does not match any existing resting order, the new order willessentially become a resting order to be matched (or attempted to bematched) with some future aggressing order. The new order will thenremain on the books for one year. On the other hand, an order specifiedas a fill or kill (e.g., if the order cannot be filled or matched withan order currently resting on the books, the order should be canceled)will never become a resting order, because it will either be filled ormatched with a currently resting order, or it will be canceled. Theamount of time needed to process or service a message once that messagehas entered the match component may be referred to as a service time.The service time for a message may depend on the state of the orderbooks when the message enters the match component, as well as thecontents, e.g., orders, that are in the message.

FIG. 5 depicts a block diagram of a system 500 for computing a forwardinterest rate for a select future time period subsequent to a currentdate. It will be appreciated that the system 500 may be a part of, orcoupled with, the Forward Rate Module 122, Match Engine Module 514/106,Order Book Module 518/110, Settlement Module 120, or other module of theexchange computing system 100 described above and shown in FIG. 1. Thesystem 500 includes a processor 502, and a non-transitory memory 504 anduser interface 506 coupled therewith, such as the processor 202, memory204 and/or interfaces 214, 216, 218 described in detail above withreference to FIG. 2.

The memory 504 being operative to store instructions, that when executedby the processor 502, cause the processor 502 to: determine a currentprice for each of a set of interest rate futures contracts havingconsecutive expiration months which collectively include the selectedfuture time period, and based thereon compute implied interest rates foreach of the consecutive expiration months; determine whether any of theconsecutive expiration months include a day on which an interest maychange based on an event unrelated to transactions involving any of theset of interest rate contracts, and for each of the expiration monthsdetermined not to include a day on which an interest may change based onan event unrelated to transactions involving any of the set of interestrate contracts, compute a baseline interest rate for each day of theexpiration period determined not to be affected by transactionsinvolving any of the set of interest rate contracts and compute anadjusted baseline interest rate for each day of the expiration perioddetermined to be affected by transactions involving any of the set ofinterest rate contracts; for each of the expiration months determined toinclude a day on which an interest may change based on an eventunrelated to transactions, starting with the expiration month furthestaway from the current date, extrapolate from the baseline and adjustedbaseline interest rates for the days of the prior and/or subsequentmonth forward and/or backward to determine extrapolated interest ratevalues for each day of the expiration month determined to include a dayon which an interest may change based on an event unrelated totransactions from the last day thereof until the day on which aninterest may change based on an event unrelated to transactions, andderiving, by the processor, a derived interest rate for each day fromthe beginning of the expiration month determined to include a day onwhich an interest may change based on an event unrelated to transactionsup to the day on which an interest may change based on an eventunrelated to transactions; and determine an initial interest rate valueaccorded to the current day; and compute, the forward interest rate forthe select future time period by combining the initial interest ratevalue, baseline interest rate value, adjusted baseline interest ratevalue, extrapolated interest rate value or derived interest rate valuefor each day of the select future time period.

More particularly, the system 500 may include an interface 506, e.g. auser interface, an instrument identification processor 508 coupled withthe interface 506, an interest rate model generator 510 coupled with theinstrument identification processor 508 and a term rate generator 512coupled with the interest rate model generator 510. One or more of theinstrument identification processor 508, interest rate model generator510 and term rate generator 512 may be implemented as a separatecomponent or as one or more logic components, such as on an FPGA whichmay include a memory or reconfigurable component to store logic and aprocessing component to execute the stored logic, or as first, second,third, fourth and fifth logic respectively, e.g. computer program logic,stored in a memory, such as the memory 204 shown in FIG. 2 and describedin more detail above with respect thereto, or other non-transitorycomputer readable medium, and executable by a processor, such as theprocessor 204 shown in FIG. 2 and described in more detail above withrespect thereto, to cause the processor 504 to, or otherwise beoperative as described.

The system 500 may operate to automatically determine an interest rateassociated with a future time period, the data transaction processingsystem comprising a system in which data items, i.e. orders to buy/sellfinancial instruments, such as interest rate futures contacts, aretransacted by a hardware matching processor, e.g. the electronic tradingsystem described above, that matches groups of electronic datatransaction request messages, including an incoming electronic datatransaction request message and one or more previously received butunmatched electronic data transaction request messages represented in adata structure, such as an order book database, stored in a memorycoupled with the hardware matching processor, for the same one of thedata items based on multiple transaction parameters, received fromdifferent client computers over a data communications network, thetransaction parameters of each of the data items specifying anexpiration time period of a set of future expiration time periods, e.g.a number of months, and a proposed transaction price indicative of aninterest rate value to be determined at the end of the specifiedexpiration time period based on a set of daily interest rates set priorthereto by a governing authority, each matched group of electronic datatransaction request messages being associated with a prevailingtransaction price indicative of an expected interest rate value to bedetermined at the end of the specified expiration time period based onthe set of daily interest rates set prior thereto by the governingauthority.

In particular, the interface 506 is operative to receive a selection ofone or more periods of time subsequent to a current date, e.g. 1, 2, 3,6 or 12 months, which may be overlapping or consecutive, for which todetermine one or more associated interest rates.

The instrument identification processor 508 is coupled with theinterface 506 and operative to determine a consecutive set of expirationtime periods which include the selected one or more periods of time andfor each expiration time period of the set, and may include the currentexpiration time period plus the subsequent requisite number ofexpiration time periods.

The instrument identification processor 508 is further operative toidentify a set of matched groups electronic data transaction requestmessages processed by the hardware matching processor and specifying theexpiration time period, computing, by the processor, an aggregate, e.g.using VWAP and/or TWAP, prevailing transaction price for the identifiedset of matched groups electronic data transaction request messages, andcompute, based on the computed aggregate prevailing transaction price,an implied expected interest rate value to be determined at the end ofthe expiration time period (R_(f)) based on a set daily interest ratesset prior thereto by a governing authority for the expiration timeperiod.

The instrument identification processor 508 is further operative todetermine a subset of the set of days (D_(T)) within the expiration timeperiod as non-adjustable days (D_(T)-D_(a)) which may be accorded abaseline expected interest rate value (R_(b)) based on transactionstransacted by the hardware matching processor, the remainder of the setof days being designated as adjustable days (D_(a)), the adjustable daysbeing known (end of month) or derived turn (seasonal adjustment) days,derived from historical data] on which a deviation from the baselineexpected interest rate value (R_(a)) may occur based on transactionstransacted by the hardware matching processor, and further determine amagnitude of the deviation (A).

The instrument identification processor 508 is further operative tocompute the baseline expected interest rate value (R_(b)) to be accordedto each of the non-adjustable days and compute, based on the magnitudeof the deviation, an adjusted baseline expected interest rate value(R_(a)) to be accord to each of the adjustable days.

The instrument identification processor 508 is further operative todetermine whether the expiration time period includes anon-transactional change day, e.g. a day on which the governingauthority, i.e. monetary policy organization meetings are scheduled tooccur, such as an FOMC meeting date, on which a deviation from thebaseline expected interest rate value may occur based on an event otherthan a transaction transacted by the hardware matching processor.

The interest rate model generator 510, coupled with the instrumentidentification processor 508, is operative to determine an initialinterest rate value as the interest rate value set for the day precedingthe current day by the governing authority, wherein if the current dayis not one of the determined non-adjustable days, adjust the initialinterest rate value based on the determined magnitude of the deviation,and accord the initial interest rate value to the current day.

The interest rate model generator 510 being operative to: for eachexpiration time period determined not to include a non-transactionalchange day, start from the current date and accord the baseline expectedinterest rate value for that expiration time period to each day of thedetermined non-adjustable days of the expiration time period and theadjusted baseline expected interest rate value to each of the adjustabledays of the expiration time period; for each expiration time perioddetermined to include a non-transactional change day, start with theexpiration time period furthest from the current date, determine thebaseline expected interest rate value, the adjusted baseline expectedinterest rate value, the extrapolated expected interest value or thederived expected interest rate value accorded to the last day of thesubsequent time period and determine an extrapolated interest rate valuefor each day prior thereto until the non-transactional change day,accord the extrapolated expected interest rate value to each day of theexpiration time period from the non-transactional change day to the lastday thereof, derive a derived expected interest rate value based on theextrapolated expected interest rate value and the implied expectedinterest rate value to be determined at the end of the expiration timeperiod, and accord the derived expected interest rate value to each dayof the expiration time period from the first day thereof until thenon-transactional change day, back to the current day; and store in adata structure stored in a memory 504 coupled with the interest ratemodel generator 510, data indicative of the accorded one of an initialinterest rate value, baseline expected interest rate value, an adjustedbaseline expected interest rate value, an extrapolated expected interestvalue or a derived expected interest rate value in association withevery day of the set of expiration time periods, this data therebyforming an interest rate model.

The term rate generator 512, coupled with the interest rate modelgenerator 510, is operative to compute, for each of the selected one ormore time periods based on the data stored in the data structure, theoverall interest rate therefore as a combination of, i.e. aggregate,average or compounded, the initial interest rate value, the baselineexpected interest rate value, the adjusted baseline expected interestrate value, the extrapolated expected interest value or the derivedexpected interest rate value accorded to each day of selected timeperiod.

The system 500 may repeat these operations, such as daily, so as tocontinuously produce expected interest rates for a forward rollingwindow of time.

In one embodiment, the transactions transacted by the hardware matchingprocessor 514/106 comprise orders received from the different clientcomputers, via network coupled between the client computers and the datatransaction processing system, to buy or sell financial instruments.

In one embodiment, the financial instruments comprise 30 day Fed Fundsfutures contracts, 30 day Secure Overnight Funding Rate futurescontracts, 30 day SONIA futures contracts, or combinations thereof, suchas strips of consecutive, e.g. quarterly, 30 day contracts.

In one embodiment, the one or more periods of time comprise one, two,three, six or twelve months, or combinations thereof.

In one embodiment, the consecutive set of expiration time periodscomprise the current month and each of the subsequent twelve months.

In one embodiment, the processor 502 is operative to compute theaggregate prevailing transaction price using volume weighed averageprices, time weighted average prices or a combination thereof.

In one embodiment, the subset of days within the expiration time periodare determined based on historical data indicative of past transactionsprocessed by the hardware matching processor.

In one embodiment, adjustable days comprise days where seasonaldeviations from the base line expected interest rate value occur.

In one embodiment, wherein the number days in the expiration time periodD_(T), the number of adjustable days in the expiration time periodD_(a), the number of non-adjustable days in the expiration time period(D_(T)-D_(a)), the magnitude of the deviation from the baseline expectedinterest rate value (A), and wherein the implied expected interest ratevalue R_(f)=[D_(a)·R_(a)+(D_(T)−D_(a))·R_(b)]/D_(T), the baselineexpected interest rate value R_(b) is computed asR_(b)=[R_(f)·D_(T)−D_(a)·A]/D_(T) and the adjusted expected interestrate value R_(a) is computed as R_(a)=R_(b)+A.

In one embodiment, the non-transactional change day comprises a day onwhich the governing authority meets to discuss interest rate changes,i.e. the day on which the governing authority specifically announces theoutcome of the discussions.

In one embodiment, the stored data indicative of the baseline expectedinterest rate value, adjusted baseline expected interest rate value,extrapolated expected interest value or derived expected interest ratevalue for each day of the set of expiration time periods comprises aninterest rate model.

In one embodiment, the processor 502 combines the retrieved initialinterest rate value, baseline expected interest rate value, adjustedbaseline expected interest rate value, extrapolated expected interestvalue or derived expected interest rate value for each day of the set ofexpiration time periods by one of averaging or compounding.

FIG. 6 depicts a flow chart showing operation of the system 500 of FIGS.1-5. In particular FIG. 6 shows a method, which may be computerimplemented, for automatically determining, by a data transactionprocessing system, an interest rate associated with a future timeperiod, the data transaction processing system in which data items, e.g.orders to transact (buy/sell) one or more financial instruments such asinterest rate futures contracts, are transacted by a hardware matchingprocessor 514/106 that matches groups of electronic data transactionrequest messages, including an incoming electronic data transactionrequest message and one or more previously received but unmatchedelectronic data transaction request messages represented in a datastructure 518/110 stored in a memory coupled with the hardware matchingprocessor 514/106, for the same one of the data items based on multipletransaction parameters, received from different client computers over adata communications network, the transaction parameters of each of thedata items specifying an expiration time period of a set of futureexpiration time periods, e.g. a number of consecutive months, and aproposed transaction price indicative of an interest rate value to bedetermined at the end of the specified expiration time period based on aset of daily interest rates set prior thereto by a governing authority,each matched group of electronic data transaction request messages beingassociated with a prevailing transaction price indicative of an expectedinterest rate value to be determined at the end of the specifiedexpiration time period based on the set of daily interest rates setprior thereto by the governing authority.

The operation of the system 500 may include: receiving, by a processor502, such as via an interface 506, a selection of one or more periods oftime subsequent to a current date, e.g. 1, 2, 3, 6, 12 months(overlapping or consecutive), for which to determine one or moreassociated interest rates (Block 602); determining, by the processor502, a consecutive set of expiration time periods which include theselected one or more periods of time and for each expiration time periodof the set, i.e. the current month plus one or more, e.g. 12, of theconsecutive following months (Block 604): identifying, by the processor502, a set of matched groups electronic data transaction requestmessages processed by the hardware matching processor 514/106 andspecifying the expiration time period, computing, by the processor, anaggregate, e.g. using VWAP and/or TWAP, prevailing transaction price forthe identified set of matched groups electronic data transaction requestmessages, and computing, by the processor 502 based on the computedaggregate prevailing transaction price, an implied expected interestrate value to be determined at the end of the expiration time period(R_(f)) based on a set daily interest rates set prior thereto by agoverning authority for the expiration time period (Block 606);determining, by the processor 502, a subset of the set of days (D_(T))within the expiration time period as non-adjustable days (D_(T)−D_(a))which may be accorded a baseline expected interest rate value (R_(b))based on transactions transacted by the hardware matching processor, theremainder of the set of days being designated as adjustable days(D_(a)), e.g. days being known (end of month) or derived turn (seasonaladjustment) days, derived from historical data, on which a deviationfrom the baseline expected interest rate value (R_(a)) may occur basedon transactions transacted by the hardware matching processor, andfurther determining, by the processor 502, a magnitude of the deviation(A) (Block 608); computing, by the processor 502, the baseline expectedinterest rate value (R_(b)) to be accorded to each of the non-adjustabledays and computing, based on the magnitude of the deviation, an adjustedbaseline expected interest rate value (R_(a)) to be accord to each ofthe adjustable days (Block 610); determining, by the processor 502,whether the expiration time period includes a non-transactional changeday, e.g. a day on which the governing authority, i.e. monetary policyorganization meetings are scheduled to occur, such as an FOMC meetingdate, on which a deviation from the baseline expected interest ratevalue may occur based on an event other than a transaction transacted bythe hardware matching processor (Block 612); the method furthercomprising, determining, by the processor 502, an initial interest ratevalue as the interest rate value set for the day preceding the currentday by the governing authority, wherein if the current day is not one ofthe determined non-adjustable days, adjusting, by the processor, theinitial interest rate value based on the determined magnitude of thedeviation, and according, by the processor, the initial interest ratevalue to the current day (Block 614); for each expiration time perioddetermined not to include a non-transactional change day, starting fromthe current date and according, by the processor 502, the baselineexpected interest rate value for that expiration time period to each dayof the determined non-adjustable days of the expiration time period andthe adjusted baseline expected interest rate value to each of theadjustable days of the expiration time period (Block 616); for eachexpiration time period determined to include a non-transactional changeday, starting with the expiration time period furthest from the currentdate, determining, by the processor, the baseline expected interest ratevalue, the adjusted baseline expected interest rate value, theextrapolated expected interest value or the derived expected interestrate value accorded to the last day of the subsequent time period anddetermining, by the processor 502, an extrapolated interest rate valuefor each day prior thereto until the non-transactional change day,according, by the processor, the extrapolated expected interest ratevalue to each day of the expiration time period from thenon-transactional change day to the last day thereof, deriving, by theprocessor 502, a derived expected interest rate value based on theextrapolated expected interest rate value and the implied expectedinterest rate value to be determined at the end of the expiration timeperiod, and according, by the processor 502, the derived expectedinterest rate value to each day of the expiration time period from thefirst day thereof until the non-transactional change day, back to thecurrent day (Block 618); storing, by the processor 502 in a datastructure (not shown) stored in a memory 504 coupled therewith, dataindicative of the accorded one of an initial interest rate value,baseline expected interest rate value, an adjusted baseline expectedinterest rate value, an extrapolated expected interest value or aderived expected interest rate value in association with every day ofthe set of expiration time periods [forming an interest rate model](Block 620); the method further comprising: computing, by the processor502 for each of the selected one or more time periods based on the datastored in the data structure, the overall interest rate therefore byretrieving from the memory 504 and combining, i.e aggregating, averagingor compounding, the initial interest rate value, the baseline expectedinterest rate value, the adjusted baseline expected interest rate value,the extrapolated expected interest value or the derived expectedinterest rate value accorded to each day of selected time period (Block622).

In one embodiment of the operation of the system 500, the transactionstransacted by the hardware matching processor 514/106 comprise ordersreceived from the different client computers 150-156, via network 160,162 coupled between the client computers 150-156 and the datatransaction processing system 100, to buy or sell financial instruments.

In one embodiment of the operation of the system 500, the financialinstruments comprise 30 day Fed Funds futures contracts, 30 day SecureOvernight Funding Rate futures contracts, 30 day SONIA futurescontracts, or combinations thereof, such as strips of consecutive, e.g.quarterly, 30 day contracts.

In one embodiment of the operation of the system 500, the one or moreperiods of time comprise one, two, three, six or twelve months, orcombinations thereof.

In one embodiment of the operation of the system 500, the consecutiveset of expiration time periods comprise the current month and each ofthe subsequent twelve months.

In one embodiment of the operation of the system 500, the processor 502is operative to compute the aggregate prevailing transaction price usingvolume weighed average prices, time weighted average prices or acombination thereof.

In one embodiment of the operation of the system 500, the subset of dayswithin the expiration time period are determined based on historicaldata indicative of past transactions processed by the hardware matchingprocessor.

In one embodiment of the operation of the system 500, the adjustabledays comprise days where seasonal deviations from the base line expectedinterest rate value occur.

In one embodiment of the operation of the system 500, wherein the numberdays in the expiration time period (D_(T)), the number of adjustabledays in the expiration time period (D_(a)), the number of non-adjustabledays in the expiration time period (D_(T)−D_(a)), the magnitude of thedeviation from the baseline expected interest rate value (A), andwherein the implied expected interest rate value(R_(f)=[D_(a)·R_(a)+(D_(T)−D_(a))·R_(b)]/D_(T)), the baseline expectedinterest rate value (R_(b)) is computed as(R_(b)=[R_(f)·D_(T)−D_(a)·A]/D_(T)) and the adjusted expected interestrate value (R_(a)) is computed as (R_(a)=R_(b) A).

In one embodiment of the operation of the system 500, thenon-transactional change day comprises a day on which the governingauthority meets to discuss interest rate changes.

In one embodiment of the operation of the system 500, the stored dataindicative of the baseline expected interest rate value, adjustedbaseline expected interest rate value, extrapolated expected interestvalue or derived expected interest rate value for each day of the set ofexpiration time periods comprises an interest rate model.

In one embodiment of the operation of the system 500, the processor 502combines the retrieved initial interest rate value, baseline expectedinterest rate value, adjusted baseline expected interest rate value,extrapolated expected interest value or derived expected interest ratevalue for each day of the set of expiration time periods by one ofaveraging or compounding.

In one embodiment of the operation of the system 500 further includesrepeating, by the processor 502, the above method on each calendar day.

As was discussed above, the described embodiments may be used with othertypes of available futures contracts, such as contracts having shorteror longer terms, and/or more or fewer available nearest expiringcontracts. That is, if less than 13 nearest one month contracts areavailable and/or suitable for use, as described above, available longerterm contracts, such as quarterly (3 month) contracts, e.g. 3 month SOFRcontracts, may be utilized to provide the requisite scope for computinga complete forward model, as described above, for a given future timeperiod.

For example, as opposed to having 1 month contracts for the nearest 13months available, the disclosed embodiments may be used in situationswhere one (1) month contracts may only be available for the nearestseven (7) months and quarterly (3 month term) contracts are availablefor the nearest six (6) quarters (nearest 6 March Cycle IMM Quarterlydates) as shown in FIG. 8. While, as described above, one monthcontracts settle based on an average overnight rate for each day of thesettlement month, the example quarterly contracts settle based on acompounded interest rate for the days of the settlement quarter.Accordingly, as will be described, the described process for computingthe forward interest rate must account for the combination of one monthand quarterly contracts as well as the different methods by which thesettlement interest rate is computed.

In particular, for the desired forward period, e.g. 1 year, the nearestavailable one month contracts are identified, e.g. the nearest 7 onemonth contracts, which encompass the current date plus two good businessdays plus the subsequent 6 months, i.e. T+2 days+6 months. A “goodbusiness day” is the next business day following a weekend or recognizedholiday. In one embodiment, where the maturity date is not a goodbusiness day, the date is adjusted to the next business day unless thatnext business day follows in the next month, in which case, the priorbusiness day is used instead.

Subsequent to the 6 month period covered by the available one monthcontracts, the remainder of the desired forward period is addressed byavailable quarterly (3 month) contracts for the same time period. Aswill be described, in this situation, the disclosed embodiments operatesimilarly as described above, i.e. when utilizing 13 one monthcontracts, but additionally account for the transition between theaverage interest rate calculation used in settlement of the one monthcontracts and the compounded interest rate used in settlement of thequarterly contracts. It will be appreciated that, depending on thecurrent date from which the model is generated, the last one monthcontract may overlap with the nearest quarterly contract or the cut overfrom the one month contracts may align with the start date of thenearest quarterly contract.

Where the 6 month period covered by one month contracts ends at the endof the quarter, i.e. there is no overlap between the last one monthcontract and the nearest quarterly contract, baseline and turn-adjustedrates for all days of the desired forward period may be computed asdescribed above where, for the quarterly contracts, the compoundedinterest rate is used to extrapolate daily rates from meeting dates aswas described above.

Where the end of the 6 month period covered by the 1 month contractsoverlaps with the nearest quarterly contract: for all days prior to the6 month date, daily rates are ascribed based on the one month contractsas described; These daily rates are then used to imply the daily ratesfor the days remaining, subsequent to the 6 month date, in the quartercovered by the quarterly contract constrained to a constant overnightrate which equates to the implied interest rate for the particularquarterly contract.

Generally then, the disclosed embodiments, whether using all of one typeof contract, e.g. all 1 month contracts, or a mix of 1 and 3 monthcontracts as described, operate in a similar manner accounting for thedifference in settlement rate computation and any overlap between thelast one month contract and first three month contract. Three monthcontracts are converted to equivalent over the counter (“otc”) swapswith convexity adjustments. Interest rate futures are standardized inthat the value of 1 basis point per contract is fixed, for example to$25/bp in CME Eurodollars. In the over the counter (otc) market thevalue of a basis point varies with the level of interest rates. In theotc world this is known as convexity. In order for the rate implied byan interest rate future to accurately represent the equivalent rate inan otc transaction the futures implied rate must be adjusted for theconvexity bias. Further information on convexity is available from GalenBurghardt and William Hoskins in their seminal piece “The Convexity Biasin Eurodollar Futures” published in 1994. For the month overlapping the6 month date some granularity is inferred from the 1 month contract withrates assigned to dates up to the 6 month date as described. Thisprovides constant rates through the entire forward period, e.g. 12months from the current date, allowing for calculation of projected OSrates for, for example, 1, 2, 3, 6 and 12 months forward as wasdescribed above.

FIG. 9 shows a graph depicting overnight rates assigned to each day in aone year period that would aggregate, via either compounding oraveraging with or without adjustment for month end turns and using amethod that seeks to apply a consistent rate between FOMC communicationdates or a consistent rate during each calendar month.

It will be appreciated that the above embodiment may be utilized withfewer, e.g., less than 7, nearest (1) month contracts, utilizingadditional quarterly, or other available longer term contracts, asdescribed.

Furthermore, it may be assumed that the provision of inter-commodityspreads between 1 month and 3 month SOFR futures in addition tointer-commodity spreads between Eurodollar futures and 3 month SOFRfutures and between Fed Funds Futures and 1 month SOFR futures will,over time, lead to consistent no-arbitrage pricing of both the 1 monthfutures strip and the 3 month futures strip used in the embodimentsdescribed above. Nevertheless, it may be determined that combiningliquidity from both the 1 and 3 month strips of futures, and/or allowingeach strip to police the integrity of the other, helps to make any termrates derived therefrom, e.g. using the above disclosed embodiments, tobe of improved quality/accuracy.

In particular, in one embodiment, the 1 month and 3 month input data maybe combined to improve the validity of the output rates. For example,first two methods may be defined for calculating rates from a 1 monthstrip and one method for a 3 month strip. For the 1 month strip, first asimple method may be provided for assigning rates on a constantovernight basis over the period of each future. Secondly, constantovernight rates may be assigned between FOMC meeting communication datesusing the bootstrapping method defined previously.

For the 3 month strip, it may be seen that the options are more limited.Since there is a possibility of more than one FOMC meeting occurringwithin the period defined by a particular 3 month futures contract, itmay be impossible to find a single solution that allows assignment ofovernight rates between FOMC meeting dates thus only the option ofassigning constant overnight rates for the period of the entire futuremay be available.

Calculation of Sets of Term Rates: From 1 month strip data—There are twomethods described for the calculation from 1-month strip data: First isthe simple starting point of having a constant overnight rate withineach calendar month—this is simple because each calendar month is aninput; and second is the more complex method of seeking to assign aconstant overnight rate between FOMC communication dates as described inthe initial embodiments over a 12 month period.

-   -   A convexity adjustment may be necessary, wherein initially at        low rates the adjustment may be zero, the source adjustments may        be derived from a volatility surface on the underlying rate, it        will take some time for that to develop, alternatively a scaling        or other modification of rates used for Eurodollars may be used,        Bloomberg may be one source for these inputs. Where the        adjustment may initially not exist, zero may be used or        alternatively a modification of the adjustments used in other        similar markets such as Eurodollars may be used;    -   Adjustments for end of month turns or other known        structural/predictable variations may be made based on        predetermined and published methods, such as a defined look back        period to determine the average adjustment realized over said        look back period, these may be reviewed, amended and agreed by a        benchmark administration governance committee;    -   Constant overnight rate for each day within a futures month        assigns an overnight rate to each day;    -   Compounding of assigned overnight rates is used to derive set of        term rates;    -   Will only provide the set of term rates with maturities up to 6        months initially due to there only being only 7 contracts        listed;    -   Convexity adjustment may be necessary (as above except that the        constraint is constant overnight rate between FOMC meeting        dates);    -   It should be possible to make adjustment for end of month turns        or other known structural/predictable variations (as noted        above);    -   A constant overnight rate is assigned for each day between known        FOMC communication dates;    -   Months that do not contain FOMC meetings have overnight rates        assigned first derived from convexity adjusted futures price,        then rates are extrapolated forwards and backwards as necessary        to FOMC communication dates, then remainder of month is        calculated from the futures price and the assigned rate from the        previous or next known period;    -   Compounding of assigned overnight rates to derive set of term        rates; and    -   Will only provide the set of term rates with maturities up to 6        months initially due to there only being only 7 contracts        listed.

Calculation of Sets of Term Rates: From 3 month strip data

-   -   Convexity adjustment may be necessary (as above);    -   It should be possible to make adjustment for end of month turns        or other known structural/predictable variations;    -   Constant overnight rate for each day within a futures quarterly        period assigns an overnight rate to each day;    -   Compounding of assigned overnight rates to derive set of term        rates; and    -   Availability of contact data covering longer than 12 months (ie        up to 5 years) assures the possibility of producing a set of        term rate covering all maturities of LIBOR up to and including        12 months

Now we consider 3 methods for combining the outputs of these approaches

Method 1

-   -   Calculate sets of term rates independently from 1 month strip        data and 3 month strip data.    -   Use either of the above described 1 month methods and the 3        month method.    -   Take a simple average of the output sets of term rates    -   Alternatively produce a trade weighted average (based on the        ratio of volumes in each strip).

Method 2

-   -   Calculate using the 1 month strip using the constant overnight        rate between FOMC communication dates method only for greatest        granularity.    -   Use the assigned rates to calculate the implied price of the 3        month strips futures to check validity.    -   If the implied prices are tolerably close, i.e., within the        minimum bid offer of the futures contract the set of term rates        from the 1 month strip may be accepted. If the rates are outside        of tolerance adjustment may be made.        -   Adjustment of the term rates by half the difference between            actual rate implied by 3 month futures strip and the rates            of the 3 month strip implied by the 1 month strip.        -   Adjustment by a weighting of this difference determined by            the average volumes traded in the two strips

Method 3

-   -   Note that near the start of a month (or the end of the previous        month) rates implied by the 1 month strip using a constant        overnight rate for the whole month will be near accurate because        of the close alignment of the contract dates with the maturities        of the set of term rates. On the first day of each month the        term rate implied by the prices of the first 6 monthly futures        should provide a perfectly accurate set of term rates for the        required maturities up to 6 months (1 month, 2 months, 3 months        and 6 months).    -   Similarly, it is noted that close to 3 month quarterly IMM dates        the rate implied by the 3 month futures strip should be very        accurate for the prediction of the term rates of the 3 month, 6        month and 12 month maturities.    -   Method 3 calculates the term rates for the 1 month and 3 months        strips using the constant overnight method for the futures        period. The inputs are then weighted based on the proximity of        the current date to either the IMM date or to month start/end.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is therefore intended that the foregoing detailed description beregarded as illustrative rather than limiting, and that it be understoodthat it is the following claims, including all equivalents, that areintended to define the spirit and scope of this invention.

What is claimed is:
 1. A computer implemented method comprising:automatically determining, by a data transaction processing system inwhich data items are transacted by a hardware matching processor, anoverall interest rate associated with a selection of one or more periodsof time subsequent to a current date, the hardware matching processormatching groups of electronic data transaction request messages to buyor sell the data items, the electronic data transaction request messagesreceived from different client computers over a data communicationsnetwork, each of the groups of the electronic data transaction requestmessages including an incoming electronic data transaction requestmessage and one or more previously received but unmatched electronicdata transaction request messages represented in a data structure storedin a memory coupled with the hardware matching processor, each of theelectronic data transaction request messages having multiple transactionparameters, the multiple transaction parameters specifying a one timeperiod of a set of available future expiration time periods and aproposed transaction price indicative of an expected settlement interestrate value, the actual settlement interest rate value determined at theend of the specified one time period based on a daily interest rate setthe day preceding the end of the specified one time period by agoverning authority, the hardware matching processor matching each ofthe groups of the electronic data transaction request messages for oneof the data items based on the multiple transaction parameters, eachmatched group of electronic data transaction request messages beingassociated with a prevailing transaction price indicative of theexpected settlement interest rate value, the automatically determiningof the overall interest rate further comprising: receiving, by ahardware forward rate processor separate from the hardware matchingprocessor, the selection of the one or more periods of time subsequentto the current date, wherein the determination of one or more associatedinterest rates is based on the selection of the one or more periods oftime subsequent to the current date; determining, by the hardwareforward rate processor, a consecutive set of expiration time periodswhich include at least the selected one or more periods of time and foreach expiration time period of the consecutive set of expiration timeperiods: identifying, by the hardware forward rate processor, a set ofmatched groups of the electronic data transaction request messagesprocessed by the hardware matching processor based on the specified onetime period of each of the electronic data transaction request messagesof the set of the matched groups, wherein the specified one time periodof each of the electronic transaction request messages includes theexpiration time period, computing, by the hardware forward rateprocessor, an aggregate prevailing transaction price for the identifiedset of matched groups electronic data transaction request messages, andcomputing, by the hardware forward rate processor based on the computedaggregate prevailing transaction price, an aggregate implied expectedinterest rate value determined for the end of the specified one timeperiod; determining, by the hardware forward rate processor, a subset ofthe set of days within the expiration time period as non-adjustabledays, each of the non-adjustable days being accorded a baseline expectedinterest rate value computed based on transactions transacted by thehardware matching processor, the remainder of the set of days beingdesignated as adjustable days on which a deviation from the baselineexpected interest rate value occurs based on transactions transacted bythe hardware matching processor, and further determining, by thehardware forward rate processor, a magnitude of the deviation based on ahistorical data of transactions transacted by the hardware matchingprocessor during the adjustable days; computing, by the hardware forwardrate processor, based on the magnitude of the deviation, an adjustedbaseline expected interest rate value being accorded to each of theadjustable days; and determining, by the hardware forward rateprocessor, whether the expiration time period includes anon-transactional change day on which a non-transactional deviation fromthe baseline expected interest rate value occurs based on an event otherthan a transaction transacted by the hardware matching processor; themethod further comprising: determining, by the hardware forward rateprocessor, an initial interest rate value as the interest rate value setfor the day preceding the current day by the governing authority,wherein if the current day is not one of the determined non-adjustabledays and if the current day is one of the determined adjustable days,adjusting, by the hardware forward rate processor, the initial interestrate value based on the determined magnitude of the deviation, andaccording, by the hardware forward rate processor, the initial interestrate value to the current day; for each expiration time perioddetermined not to include a non-transactional change day, starting fromthe current date, according, by the hardware forward rate processor, thebaseline expected interest rate value for that expiration time period toeach day of the determined non-adjustable days of the expiration timeperiod and the adjusted baseline expected interest rate value to each ofthe adjustable days of the expiration time period; for each expirationtime period determined to include a non-transactional change day,starting with the expiration time period furthest from the current date,determining, by the hardware forward rate processor, an extrapolatedinterest rate value for each day prior thereto until thenon-transactional change day, the extrapolated interest value determinedbased on the baseline expected interest rate value or the adjustedbaseline expected interest rate value accorded to the last day of thesubsequent time period, and according, by the hardware forward rateprocessor, the extrapolated expected interest rate value to each day ofthe expiration time period from the non-transactional change day to thelast day thereof, and further deriving, by the hardware forward rateprocessor, a derived expected interest rate value based on theextrapolated expected interest rate value and the aggregate impliedexpected interest rate value determined for the end of the expirationtime period, and according, by the hardware forward rate processor, thederived expected interest rate value to each day of the expiration timeperiod from a first day thereof until the non-transactional change day,back to the current day; and storing, by the hardware forward rateprocessor in a data structure stored in a memory coupled therewith, forevery day of the set of the expiration time periods including thecurrent day, the adjustable and the non-adjustable days, data indicativeof the accorded one of the initial interest rate value, the baselineexpected interest rate value, the adjusted baseline expected interestrate value, the extrapolated expected interest value or the derivedexpected interest rate value in association with every day of the set ofexpiration time periods; and the method further comprising: computing,by the hardware forward rate processor for each of the selected one ormore time periods based on the data stored in the data structure, theoverall interest rate by retrieving from the memory and combining thedata indicative of the accorded one of the initial interest rate value,the baseline expected interest rate value, the adjusted baselineexpected interest rate value, the extrapolated expected interest valueor the derived expected interest rate value for each day of the selectedone or more time periods.
 2. The computer implemented method of claim 1wherein the transactions transacted by the hardware matching processorcomprise orders received from the different client computers, via thedata communications network coupled between the client computers and thedata transaction processing system, to buy or sell financialinstruments.
 3. The computer implemented method of claim 2 wherein thefinancial instruments comprise 30 day Fed Funds futures contracts, 30day Secure Overnight Funding Rate futures contracts, 30 day SONIAfutures contracts, or combinations thereof.
 4. The computer implementedmethod of claim 1 wherein the one or more periods of time comprise one,two, three, six or twelve months, or combinations thereof.
 5. Thecomputer implemented method of claim 1 wherein the consecutive set ofthe expiration time periods comprise a current month and each of thesubsequent twelve months.
 6. The computer implemented method of claim 1wherein the hardware forward rate processor is operative to compute theaggregate prevailing transaction price using volume weighed averageprices, time weighted average prices or a combination thereof.
 7. Thecomputer implemented method of claim 1 wherein the subset of the set ofdays within the expiration time period are determined based onhistorical data indicative of past transactions processed by thehardware matching processor.
 8. The computer implemented method of claim1 wherein adjustable days comprise days where seasonal deviations fromthe baseline expected interest rate value occur.
 9. The computerimplemented method of claim 1, wherein the aggregate implied expectedinterest rate value (R_(f)) is determined asR_(f)=[D_(a)·R_(a)+(D_(T)−D_(a))·R_(b)]/D_(T), where D_(T) is the numberof days in the expiration time period, D_(a) is the number of adjustabledays in the expiration time period, the number of non-adjustable days inthe expiration time period is computed as (D_(T)−D_(a)), R_(b) is thebaseline expected interest rate value, R_(a) is the adjusted expectedinterest rate value and (A) is the magnitude of the deviation from thebaseline expected interest rate value, where solving for R_(a) andR_(b):R _(b) is computed as R _(b)=[R _(f) ·D _(T) −D _(a) ·A]/D _(T) andR _(a) is computed as R _(a) =R _(b) +A.
 10. The computer implementedmethod of claim 1 wherein the non-transactional change day comprises aday on which the governing authority meets and discusses interest ratechanges.
 11. The computer implemented method of claim 1 wherein thestored data indicative of the accorded one of the baseline expectedinterest rate value, the adjusted baseline expected interest rate value,the extrapolated expected interest value or the derived expectedinterest rate value for each day of the set of the expiration timeperiods comprises an interest rate model.
 12. The computer implementedmethod of claim 1 wherein the hardware forward rate processor combinesthe retrieved initial interest rate value, baseline expected interestrate value, adjusted baseline expected interest rate value, extrapolatedexpected interest value, or derived expected interest rate value foreach day of the set of expiration time periods by one of averaging orcompounding.
 13. The computer implemented method of claim 1 furthercomprising repeating, by the hardware forward rate processor, the methodon each day.
 14. A system comprising: a data transaction processingsystem, in which data items are transacted by a hardware matchingprocessor, that automatically determines an overall interest rateassociated with a selection of one or more periods of time subsequent toa current date, the hardware matching processor matching groups ofelectronic data transaction request messages to buy or sell the dataitems, the electronic data transaction request messages received fromdifferent client computers over a data communications network, each ofthe groups of the electronic data transaction request messages includingan incoming electronic data transaction request message and one or morepreviously received but unmatched electronic data transaction requestmessages represented in a data structure stored in a memory coupled withthe hardware matching processor, each of the electronic data transactionrequest messages having multiple transaction parameters, the multipletransaction parameters specifying a one time period of a set ofavailable future expiration time periods and a proposed transactionprice indicative of an expected settlement interest rate value, theactual settlement interest rate value determined at the end of thespecified one time period based on a daily interest rate set the daypreceding the end of the specified one time period by a governingauthority, the hardware matching processor matching each of the groupsof the electronic data transaction request messages for one of the dataitems based on the multiple transaction parameters, each matched groupof electronic data transaction request messages being associated with aprevailing transaction price indicative of the expected settlementinterest rate value, the data transaction system further comprising: asecond non-transitory memory; a hardware forward rate processor separatefrom the hardware matching processor and coupled with the secondnon-transitory memory, the hardware forward rate processor executinginstructions stored in the second non-transitory memory to implement: auser interface operative to receive the selection of the one or moreperiods of time subsequent to the current date, wherein thedetermination of one or more associated interest rates is based on theselection of the one or more periods of time subsequent to the currentdate; an instrument identification processor coupled with the userinterface and operative to determine a consecutive set of expirationtime periods which include at least the selected one or more periods oftime and for each expiration time period of the consecutive set ofexpiration time periods: identify a set of matched groups of theelectronic data transaction request messages processed by the hardwarematching processor based on the specified one time period of each of theelectronic data transaction request messages of the set of the matchedgroups, wherein the specified one time period of each of the electronictransaction request messages includes the expiration time period,computing an aggregate prevailing transaction price for the identifiedset of matched groups electronic data transaction request messages, andcompute, based on the computed aggregate prevailing transaction price,an aggregate implied expected interest rate value determined for the endof the specified one time period; determine a subset of the set of dayswithin the expiration time period as non-adjustable days, each of thenon-adjustable day being accorded a baseline expected interest ratevalue computed based on transactions transacted by the hardware matchingprocessor, the remainder of the set of days being designated asadjustable days on which a deviation from the baseline expected interestrate value occurs based on transactions transacted by the hardwarematching processor, and further determine a magnitude of the deviationbased on a historical data of transactions transacted by the hardwarematching processor during the adjustable days; compute, based on themagnitude of the deviation, an adjusted baseline expected interest ratevalue being accorded to each of the adjustable days; determine whetherthe expiration time period includes a non-transactional change day onwhich a deviation from the baseline expected interest rate value occursbased on an event other than a transaction transacted by the hardwarematching processor; the system further comprising an interest rate modelgenerator implemented by the hardware forward rate processor and coupledwith the instrument identification processor and operative to: determinean initial interest rate value as the interest rate value set for theday preceding the current day by the governing authority, wherein if thecurrent day is not one of the determined non-adjustable days, and if thecurrent day is one of the determined adjustable days, adjust the initialinterest rate value based on the determined magnitude of the deviation,and accord the initial interest rate value to the current day; whereinthe interest rate model generator is further operative to: for eachexpiration time period determined not to include a non-transactionalchange day, start from the current date and accord the baseline expectedinterest rate value for that expiration time period to each day of thedetermined non-adjustable days of the expiration time period and theadjusted baseline expected interest rate value to each of the adjustabledays of the expiration time period; for each expiration time perioddetermined to include a non-transactional change day, start with theexpiration time period furthest from the current date, determine anextrapolated interest rate value for each day prior thereto until thenon-transactional change day, the extrapolated interest value determinedbased on the baseline expected interest rate value or the adjustedbaseline expected interest rate value accorded to the last day of thesubsequent time period, and accord the extrapolated expected interestrate value to each day of the expiration time period from thenon-transactional change day to the last day thereof, and further derivea derived expected interest rate value based on the extrapolatedexpected interest rate value and the implied expected interest ratevalue determined at the end of the expiration time period, and accordthe derived expected interest rate value to each day of the expirationtime period from a first day thereof until the non-transactional changeday, back to the current day; and store in a data structure stored in amemory coupled with the interest rate model generator, for every day ofthe set of the expiration time periods including the current day, theadjustable and the non-adjustable days, data indicative of the accordedone of the initial interest rate value, the baseline expected interestrate value, the adjusted baseline expected interest rate value, theextrapolated expected interest value or the derived expected interestrate value in association with every day of the set of expiration timeperiods; and the system further comprising a term rate generatorimplemented by the forward rate processor coupled with the interest ratemodel generator and operative to compute, for each of the selected oneor more time periods based on the data stored in the data structure, theoverall interest rate therefore as a combination of the data indicativeof the accorded one of the initial interest rate value, the baselineexpected interest rate value, the adjusted baseline expected interestrate value, the extrapolated expected interest value or the derivedexpected interest rate value for each day of selected one or more timeperiods.
 15. The system of claim 14 wherein the transactions transactedby the hardware matching processor comprise orders received from thedifferent client computers, via the data communications network coupledbetween the client computers and the data transaction processing system,to buy or sell financial instruments.
 16. The system of claim 15 whereinthe financial instruments comprise 30 day Fed Funds futures contracts,30 day Secure Overnight Funding Rate futures contracts, 30 day SONIAfutures contracts, or combinations thereof.
 17. The system of claim 14wherein the one or more periods of time comprise one, two, three, six ortwelve months, or combinations thereof.
 18. The system of claim 14wherein the consecutive set of expiration time periods comprise thecurrent month and each of the subsequent twelve months.
 19. The systemof claim 14 wherein the instrument identification processor is operativeto compute the aggregate prevailing transaction price using volumeweighed average prices, time weighted average prices or a combinationthereof.
 20. The system of claim 14 wherein the subset of the set ofdays within the expiration time period are determined based onhistorical data indicative of past transactions processed by thehardware matching processor.
 21. The system of claim 14 whereinadjustable days comprise days where seasonal deviations from thebaseline expected interest rate value occur.
 22. The system of claim 14,wherein the aggregate implied expected interest rate value (R_(f)) isdetermined as R_(f)=[D_(a)·R_(a)+(D_(T)−D_(a))·R_(b)]/D_(T), where D_(T)is the number of days in the expiration time period, D_(a) is the numberof adjustable days in the expiration time period, the number ofnon-adjustable days in the expiration time period is computed as(D_(T)−D_(a)), R_(b) is the baseline expected interest rate value, R_(a)is the adjusted expected interest rate value and (A) is the magnitude ofthe deviation from the baseline expected interest rate value, wheresolving for R_(a) and R_(b):R _(b) is computed as R _(b)=[R _(f) ·D _(T) −D _(a) ·A]/D _(T) andR _(a) is computed as R _(a) =R _(b) +A.
 23. The system of claim 14wherein the non-transactional change day comprises a day on which thegoverning authority meets and discusses interest rate changes.
 24. Thesystem of claim 14 wherein the stored data indicative of the accordedone of the baseline expected interest rate value, the adjusted baselineexpected interest rate value, the extrapolated expected interest valueor the derived expected interest rate value for each day of the set ofexpiration time periods comprises an interest rate model.
 25. The systemof claim 14 wherein the term rate generator combines the retrievedinitial interest rate value, the baseline expected interest rate value,the adjusted baseline expected interest rate value, the extrapolatedexpected interest value or the derived expected interest rate value foreach day of the set of the selected expiration time periods by one ofaveraging or compounding.
 26. The system of claim 14 further comprisingrepeating, by the forward rate processor, the operations of theinstrument identification processor, interest rate model generator andterm rate generator on each day.